From 0ad455010c7ad5dfffdf3f2e7f9bb069a5f59c34 Mon Sep 17 00:00:00 2001
From: spolisar <22416070+spolisar@users.noreply.github.com>
Date: Fri, 27 Feb 2026 16:11:33 -0500
Subject: [PATCH 01/17] feat: add support for PatchTST-FM

---
 timecopilot/models/foundation/patchtst_fm.py | 320 +++++++++++++++++++
 1 file changed, 320 insertions(+)
 create mode 100644 timecopilot/models/foundation/patchtst_fm.py

diff --git a/timecopilot/models/foundation/patchtst_fm.py b/timecopilot/models/foundation/patchtst_fm.py
new file mode 100644
index 0000000..b20cb17
--- /dev/null
+++ b/timecopilot/models/foundation/patchtst_fm.py
@@ -0,0 +1,320 @@
+from contextlib import contextmanager
+
+import numpy as np
+import pandas as pd
+import torch
+from gluonts.transform import LastValueImputation
+from tqdm import tqdm
+from tsfm_public import PatchTSTFMForPrediction
+
+from timecopilot.models.foundation.utils import TimeSeriesDataset
+from timecopilot.models.utils.forecaster import Forecaster, QuantileConverter
+
+# default to the median quantile
+# PatchTST-FM supports quantiles from 0.01 to 0.99
+DEFAULT_QUANTILES = [0.5]
+
+
+class PatchTSTFM(Forecaster):
+    """
+    FlowState is the first time-scale adjustable Time Series Foundation Model (TSFM),
+    open-sourced by IBM Research. Combining a State Space Model (SSM) Encoder with a
+    Functional Basis Decoder allows FlowState to transition into a timescale invariant
+    coefficient space and make a continuous forecast from this space. This allows
+    FlowState to seamlessly adjust to all possible sampling rates.
+
+    See the [official repo](https://github.com/ibm-granite/granite-tsfm) and
+    [paper](https://arxiv.org/abs/2508.05287) for more details.
+    """
+
+    # NOTE: may want to adjust default context_length, default on granite_tsfm is 8192
+    def __init__(
+        self,
+        repo_id: str = "ibm-research/patchtst-fm-r1",
+        # scale_factor: float | None = None,
+        context_length: int = 2_048,
+        batch_size: int = 2_048,
+        alias: str = "PatchTST-FM",
+    ):
+        """
+        Initialize PatchTSTFM time series foundation model.
+
+        Args:
+            repo_id (str, optional): The Hugging Face Hub model ID or local path to
+                load the PatchTST-FM model from. Supported models:
+
+                - `ibm-research/patchtst-fm-r1`
+
+            context_length (int, optional): Maximum context length (input window size)
+                for the model. Controls how much history is used for each forecast.
+                Defaults to 2,048. The model supports flexible context lengths.
+            batch_size (int, optional): Batch size for inference. Defaults to 2,048.
+                Adjust based on available memory and model size. Larger batch sizes
+                can improve throughput but require more GPU memory.
+            alias (str, optional): Name to use for the model in output DataFrames and
+                logs. Defaults to "PatchTST-FM".
+
+        Notes:
+            **Academic Reference:**
+
+            - Paper: [Revisiting the Generic Transformer: Deconstructing a
+            Strong Baseline for Time Series Foundation Models](
+            https://arxiv.org/abs/2602.06909)
+
+            **Resources:**
+
+            - GitHub: [ibm-granite/granite-tsfm](https://github.com/ibm-granite/granite-tsfm)
+            - HuggingFace Models: [ibm-research/patchtst-fm-r1](https://huggingface.co/ibm-research/patchtst-fm-r1)
+
+            **Technical Details:**
+
+            - The model is loaded onto the best available device (GPU if
+              available, otherwise CPU).
+
+            **Supported Models:**
+
+            - `ibm-research/patchtst-fm-r1` (default)
+        """
+        self.repo_id = repo_id
+        # self.scale_factor = scale_factor
+        self.context_length = context_length
+        self.batch_size = batch_size
+        # NOTE: 'mps' may not be 100% reliable, initial tests with the
+        # patchtst-fm gift_eval notebook resulted in predictions of 0 across
+        # the board. for now use mps when available, change if it becomes an issue.
+        # self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.device = (
+            "cuda"
+            if torch.cuda.is_available()
+            else ("mps" if torch.mps.is_available() else "cpu")
+        )
+        self.alias = alias
+        self.dtype = torch.float32
+
+    @contextmanager
+    def _get_model(self) -> PatchTSTFMForPrediction:
+        model = PatchTSTFMForPrediction.from_pretrained(self.repo_id).to(self.device)
+        try:
+            model.eval()
+            yield model
+        finally:
+            del model
+            if self.device.startswith("cuda"):
+                torch.cuda.empty_cache()
+            elif self.device.startswith("mps"):
+                torch.mps.empty_cache()
+
+    def _left_pad_and_stack_1D(self, tensors: list[torch.Tensor]) -> torch.Tensor:
+        max_len = max(len(c) for c in tensors)
+        padded = []
+        for c in tensors:
+            assert isinstance(c, torch.Tensor)
+            assert c.ndim == 1
+            padding = torch.full(
+                size=(max_len - len(c),),
+                fill_value=torch.nan,
+                device=c.device,
+                dtype=c.dtype,
+            )
+            padded.append(torch.concat((padding, c), dim=-1))
+        return torch.stack(padded)
+
+    def _prepare_and_validate_context(
+        self,
+        context: list[torch.Tensor] | torch.Tensor,
+    ) -> torch.Tensor:
+        if isinstance(context, list):
+            context = self._left_pad_and_stack_1D(context)
+        assert isinstance(context, torch.Tensor)
+        if context.ndim == 1:
+            context = context.unsqueeze(0)
+        assert context.ndim == 2
+        return context
+
+    def _maybe_impute_missing(
+        self, batch: torch.Tensor, dtype=torch.float32
+    ) -> torch.Tensor:
+        if torch.isnan(batch).any():
+            batch = batch.to(dtype=dtype).numpy(force=True)
+            imputed_rows = []
+            for i in range(batch.shape[0]):
+                row = batch[i]
+                imputed_row = LastValueImputation()(row)
+                imputed_rows.append(imputed_row)
+            batch = np.vstack(imputed_rows)
+            batch = torch.tensor(
+                batch,
+                dtype=self.dtype,
+                device=self.device,
+            )
+        return batch
+
+    def _predict_batch(
+        self,
+        model: PatchTSTFMForPrediction,
+        batch: list[torch.Tensor] | torch.Tensor,
+        h: int,
+        quantiles: list[float] | None,
+        supported_quantiles: list[float],
+        # scale_factor: float,
+    ) -> tuple[np.ndarray, np.ndarray | None]:
+        context = self._prepare_and_validate_context(batch)
+        if context.shape[1] > self.context_length:
+            context = context[..., -self.context_length :]
+        context = self._maybe_impute_missing(context)
+        # context is (batch, context_length)
+
+        # input data is grouped by id
+        # input shape: (id_group/batch, data)
+        # output shape: (batch/id, quantiles, h)
+        quantile_levels = DEFAULT_QUANTILES if quantiles is None else quantiles
+
+        fcst = model(
+            context,
+            prediction_length=h,
+            quantile_levels=quantile_levels,
+            # scale_factor=scale_factor,
+            # batch_first=False,
+        ).quantile_predictions
+        fcst = fcst.squeeze(-1).transpose(-1, -2)  # now shape is (batch, h, quantiles)
+
+        # may not be the ideal solution, but this should be more adaptable
+        # when quantiles can vary.
+        # there is no guarantee that 0.5 will be in the list of quantiles.
+        mean = fcst.mean(dim=2).squeeze()
+        # fcst_mean = fcst[..., quantile_levels.index(0.5)].squeeze()
+        fcst_mean = mean
+        fcst_mean_np = fcst_mean.detach().numpy(force=True)
+        fcst_quantiles_np = (
+            fcst.detach().numpy(force=True) if quantiles is not None else None
+        )
+        return fcst_mean_np, fcst_quantiles_np
+
+    def _predict(
+        self,
+        model: PatchTSTFMForPrediction,
+        dataset: TimeSeriesDataset,
+        h: int,
+        quantiles: list[float] | None,
+        supported_quantiles: list[float],
+        # scale_factor: float,
+    ) -> tuple[np.ndarray, np.ndarray | None]:
+        fcsts = [
+            self._predict_batch(
+                model,
+                batch,
+                h,
+                quantiles,
+                supported_quantiles,
+                # scale_factor,
+            )
+            for batch in tqdm(dataset)
+        ]  # list of tuples
+        fcsts_mean_tp, fcsts_quantiles_tp = zip(*fcsts, strict=False)
+        # handle single item forecast output
+        fcsts_mean_np = fcsts_mean_tp[0]
+        if fcsts_mean_tp[0].shape != tuple():
+            fcsts_mean_np = np.concatenate(fcsts_mean_tp)
+        if quantiles is not None:
+            fcsts_quantiles_np = np.concatenate(fcsts_quantiles_tp)
+        else:
+            fcsts_quantiles_np = None
+        return fcsts_mean_np, fcsts_quantiles_np
+
+    def forecast(
+        self,
+        df: pd.DataFrame,
+        h: int,
+        freq: str | None = None,
+        level: list[int | float] | None = None,
+        quantiles: list[float] | None = None,
+    ) -> pd.DataFrame:
+        """Generate forecasts for time series data using the model.
+
+        This method produces point forecasts and, optionally, prediction
+        intervals or quantile forecasts. The input DataFrame can contain one
+        or multiple time series in stacked (long) format.
+
+        Args:
+            df (pd.DataFrame):
+                DataFrame containing the time series to forecast. It must
+                include as columns:
+
+                    - "unique_id": an ID column to distinguish multiple series.
+                    - "ds": a time column indicating timestamps or periods.
+                    - "y": a target column with the observed values.
+
+            h (int):
+                Forecast horizon specifying how many future steps to predict.
+            freq (str, optional):
+                Frequency of the time series (e.g. "D" for daily, "M" for
+                monthly). See [Pandas frequency aliases](https://pandas.pydata.org/
+                pandas-docs/stable/user_guide/timeseries.html#offset-aliases) for
+                valid values. If not provided, the frequency will be inferred
+                from the data.
+            level (list[int | float], optional):
+                Confidence levels for prediction intervals, expressed as
+                percentages (e.g. [80, 95]). If provided, the returned
+                DataFrame will include lower and upper interval columns for
+                each specified level.
+            quantiles (list[float], optional):
+                List of quantiles to forecast, expressed as floats between 0
+                and 1. Should not be used simultaneously with `level`. When
+                provided, the output DataFrame will contain additional columns
+                named in the format "model-q-{percentile}", where {percentile}
+                = 100 × quantile value.
+
+        Returns:
+            pd.DataFrame:
+                DataFrame containing forecast results. Includes:
+
+                    - point forecasts for each timestamp and series.
+                    - prediction intervals if `level` is specified.
+                    - quantile forecasts if `quantiles` is specified.
+
+                For multi-series data, the output retains the same unique
+                identifiers as the input DataFrame.
+        """
+        freq = self._maybe_infer_freq(df, freq)
+        qc = QuantileConverter(level=level, quantiles=quantiles)
+        dataset = TimeSeriesDataset.from_df(
+            df,
+            batch_size=self.batch_size,
+            dtype=self.dtype,
+        )
+        fcst_df = dataset.make_future_dataframe(h=h, freq=freq)
+        # scale_factor = self.scale_factor or get_fixed_factor(freq)
+        with self._get_model() as model:
+            cfg = model.config
+            supported_quantiles = cfg.quantile_levels
+            if qc.quantiles is not None and not set(qc.quantiles).issubset(
+                supported_quantiles
+            ):
+                raise ValueError(
+                    "PatchTSTFM only supports the default quantiles, "
+                    f"supported quantiles are {supported_quantiles}, "
+                    "please use the default quantiles or default level, "
+                )
+
+            fcsts_mean_np, fcsts_quantiles_np = self._predict(
+                model,
+                dataset,
+                h,
+                quantiles=qc.quantiles,
+                supported_quantiles=supported_quantiles,
+                # scale_factor=scale_factor,
+            )
+
+        fcst_df[self.alias] = fcsts_mean_np.reshape(-1, 1)
+
+        # should only enter when quantiles are used
+        if qc.quantiles is not None and fcsts_quantiles_np is not None:
+            for i, q in enumerate(qc.quantiles):
+                fcst_df[f"{self.alias}-q-{int(q * 100)}"] = fcsts_quantiles_np[
+                    ..., i
+                ].reshape(-1, 1)
+            fcst_df = qc.maybe_convert_quantiles_to_level(
+                fcst_df,
+                models=[self.alias],
+            )
+        return fcst_df

From 8b05a322503b4772720aafeb755f3c490e5a879e Mon Sep 17 00:00:00 2001
From: spolisar <22416070+spolisar@users.noreply.github.com>
Date: Fri, 27 Feb 2026 16:16:03 -0500
Subject: [PATCH 02/17] feat: granite-tsfm version bump for patchTST-fm

---
 pyproject.toml |     2 +-
 uv.lock        | 12657 ++++++++++++++++++++++++-----------------------
 2 files changed, 6330 insertions(+), 6329 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 6f672c9..4f29d8f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -88,7 +88,7 @@ dependencies = [
   "tabpfn-time-series==1.0.3 ; python_full_version < '3.13'",
   "tensorboard>=2.20.0",
   "timecopilot-chronos-forecasting>=0.2.0",
-  "timecopilot-granite-tsfm>=0.1.1",
+  "timecopilot-granite-tsfm>=0.1.2",
   "timecopilot-timesfm>=0.2.1",
   "timecopilot-tirex>=0.1.0 ; python_full_version >= '3.11'",
   "timecopilot-toto>=0.1.5",
diff --git a/uv.lock b/uv.lock
index 81ebecd..f2eb914 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,8577 +1,8578 @@
+version = 1
+revision = 3
 requires-python = ">=3.10"
 resolution-markers = [
-  "python_full_version < '3.11' and sys_platform != 'linux'",
-  "python_full_version < '3.11' and sys_platform == 'linux'",
-  "python_full_version == '3.11.*' and sys_platform != 'linux'",
-  "python_full_version == '3.11.*' and sys_platform == 'linux'",
-  "python_full_version == '3.12.*' and sys_platform != 'linux'",
-  "python_full_version == '3.12.*' and sys_platform == 'linux'",
-  "python_full_version == '3.13.*' and sys_platform != 'linux'",
-  "python_full_version == '3.13.*' and sys_platform == 'linux'",
-  "python_full_version >= '3.14' and sys_platform != 'linux'",
-  "python_full_version >= '3.14' and sys_platform == 'linux'",
+    "python_full_version < '3.11' and sys_platform != 'linux'",
+    "python_full_version < '3.11' and sys_platform == 'linux'",
+    "python_full_version == '3.11.*' and sys_platform != 'linux'",
+    "python_full_version == '3.11.*' and sys_platform == 'linux'",
+    "python_full_version == '3.12.*' and sys_platform != 'linux'",
+    "python_full_version == '3.12.*' and sys_platform == 'linux'",
+    "python_full_version == '3.13.*' and sys_platform != 'linux'",
+    "python_full_version == '3.13.*' and sys_platform == 'linux'",
+    "python_full_version >= '3.14' and sys_platform != 'linux'",
+    "python_full_version >= '3.14' and sys_platform == 'linux'",
 ]
-revision = 3
-version = 1
 
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+    { name = "psutil" },
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 name = "timecopilot"
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 version = "0.0.23"
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+    { name = "timecopilot-toto" },
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 dev = [
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+    { name = "mkdocstrings", extra = ["python"] },
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+    { url = "https://files.pythonhosted.org/packages/f0/ef/da163ce2450ed4febf6467d77ccb4cd52c4c30ab45624bad26ca0a27260c/zstandard-0.25.0-cp314-cp314-win_arm64.whl", hash = "sha256:d7541afd73985c630bafcd6338d2518ae96060075f9463d7dc14cfb33514383d", size = 476940, upload-time = "2025-09-14T22:18:19.088Z" },
 ]

From 699bf06b69db0f53364100294d324750783d0f1d Mon Sep 17 00:00:00 2001
From: spolisar <22416070+spolisar@users.noreply.github.com>
Date: Fri, 27 Feb 2026 17:17:26 -0500
Subject: [PATCH 03/17] docs: add patchtst-fm example

---
 docs/examples/patchtst-fm.ipynb | 415 ++++++++++++++++++++++++++++++++
 1 file changed, 415 insertions(+)
 create mode 100644 docs/examples/patchtst-fm.ipynb

diff --git a/docs/examples/patchtst-fm.ipynb b/docs/examples/patchtst-fm.ipynb
new file mode 100644
index 0000000..6825929
--- /dev/null
+++ b/docs/examples/patchtst-fm.ipynb
@@ -0,0 +1,415 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "87cd2049",
+   "metadata": {},
+   "source": [
+    "# Using PatchTST-FM\n",
+    "\n",
+    "This is an example for using PatchTST-FM with the `timecopilot` library."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "45ab9d16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import nest_asyncio\n",
+    "\n",
+    "nest_asyncio.apply()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8a43f8f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " See https://github.com/google-research/timesfm/blob/master/README.md for updated APIs.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from timecopilot import TimeCopilot\n",
+    "from timecopilot.models.foundation.patchtst_fm import PatchTSTFM"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57cfe7d0",
+   "metadata": {},
+   "source": [
+    "## Setup the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4ddf583e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = PatchTSTFM()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e4c38f7",
+   "metadata": {},
+   "source": [
+    "## Create a TimeCopilot instance with PatchTST-FM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "cba895b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tc = TimeCopilot(\n",
+    "    llm=\"openai:gpt-4o\",\n",
+    "    forecasters=[\n",
+    "        model\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90b8ad9f",
+   "metadata": {},
+   "source": [
+    "## Forecasting \n",
+    "\n",
+    "Once setup is complete, you can generate forecasts with patchTST-FM."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e6bd16a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"https://timecopilot.s3.amazonaws.com/public/data/air_passengers.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5af63b5a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:p-33087:t-8427712640:_client.py:_send_single_request:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
+      "0it [00:00, ?it/s]INFO:p-33087:t-8427712640:modeling_patchtst_fm.py:forward:Context Len: 8192 | Forecast Len: 24 \n",
+      "100%|██████████| 1/1 [00:01<00:00,  1.52s/it]\n",
+      "1it [00:02,  2.37s/it]\n",
+      "1it [00:00, 185.64it/s]\n",
+      "INFO:p-33087:t-8427712640:_client.py:_send_single_request:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
+      "  0%|          | 0/1 [00:00<?, ?it/s]INFO:p-33087:t-8427712640:modeling_patchtst_fm.py:forward:Context Len: 8192 | Forecast Len: 24 \n",
+      "100%|██████████| 1/1 [00:00<00:00,  8.46it/s]\n",
+      "0it [00:00, ?it/s]INFO:p-33087:t-8427712640:modeling_patchtst_fm.py:forward:Context Len: 8192 | Forecast Len: 12 \n",
+      "100%|██████████| 1/1 [00:00<00:00,  8.72it/s]\n",
+      "1it [00:00,  2.27it/s]INFO:p-33087:t-8427712640:modeling_patchtst_fm.py:forward:Context Len: 8192 | Forecast Len: 12 \n",
+      "100%|██████████| 1/1 [00:00<00:00,  8.98it/s]\n",
+      "2it [00:00,  2.29it/s]INFO:p-33087:t-8427712640:modeling_patchtst_fm.py:forward:Context Len: 8192 | Forecast Len: 12 \n",
+      "100%|██████████| 1/1 [00:00<00:00,  8.74it/s]\n",
+      "3it [00:01,  2.28it/s]INFO:p-33087:t-8427712640:modeling_patchtst_fm.py:forward:Context Len: 8192 | Forecast Len: 12 \n",
+      "100%|██████████| 1/1 [00:00<00:00,  9.08it/s]\n",
+      "4it [00:01,  2.31it/s]INFO:p-33087:t-8427712640:modeling_patchtst_fm.py:forward:Context Len: 8192 | Forecast Len: 12 \n",
+      "100%|██████████| 1/1 [00:00<00:00,  9.14it/s]\n",
+      "5it [00:02,  2.31it/s]INFO:p-33087:t-8427712640:modeling_patchtst_fm.py:forward:Context Len: 8192 | Forecast Len: 12 \n",
+      "100%|██████████| 1/1 [00:00<00:00,  9.16it/s]\n",
+      "6it [00:02,  2.24it/s]INFO:p-33087:t-8427712640:modeling_patchtst_fm.py:forward:Context Len: 8192 | Forecast Len: 12 \n",
+      "100%|██████████| 1/1 [00:00<00:00,  9.20it/s]\n",
+      "7it [00:03,  2.26it/s]INFO:p-33087:t-8427712640:modeling_patchtst_fm.py:forward:Context Len: 8192 | Forecast Len: 12 \n",
+      "100%|██████████| 1/1 [00:00<00:00,  9.13it/s]\n",
+      "8it [00:03,  2.30it/s]INFO:p-33087:t-8427712640:modeling_patchtst_fm.py:forward:Context Len: 8192 | Forecast Len: 12 \n",
+      "100%|██████████| 1/1 [00:00<00:00,  9.24it/s]\n",
+      "9it [00:03,  2.32it/s]INFO:p-33087:t-8427712640:modeling_patchtst_fm.py:forward:Context Len: 8192 | Forecast Len: 12 \n",
+      "100%|██████████| 1/1 [00:00<00:00,  9.03it/s]\n",
+      "10it [00:04,  2.32it/s]INFO:p-33087:t-8427712640:modeling_patchtst_fm.py:forward:Context Len: 8192 | Forecast Len: 12 \n",
+      "100%|██████████| 1/1 [00:00<00:00,  9.12it/s]\n",
+      "11it [00:04,  2.30it/s]\n",
+      "INFO:p-33087:t-8427712640:_client.py:_send_single_request:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = tc.forecast(\n",
+    "    df=df,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f1d4acd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The time series 'AirPassengers' has a series_length of 144 indicating a long enough series for analysis. The high stability value of 0.933 implies consistent behavior over time with a low susceptibility to random fluctuations. Entropy being low at 0.4287 indicates predictability. High autocorrelation factors like x_acf1 (0.948) and seas_acf1 (0.760) corroborate pronounced seasonal patterns, typical in monthly airline passenger data. Holt-Winters parameters suggest significant trend (alpha close to 1) and seasonality (gamma at 0.747) but no trend smoothing (beta=0).\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(result.output.tsfeatures_analysis)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "5bad75f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>unique_id</th>\n",
+       "      <th>ds</th>\n",
+       "      <th>PatchTST-FM</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>AirPassengers</td>\n",
+       "      <td>1961-01-01</td>\n",
+       "      <td>455.334595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AirPassengers</td>\n",
+       "      <td>1961-02-01</td>\n",
+       "      <td>426.086121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AirPassengers</td>\n",
+       "      <td>1961-03-01</td>\n",
+       "      <td>480.885254</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>AirPassengers</td>\n",
+       "      <td>1961-04-01</td>\n",
+       "      <td>490.008148</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>AirPassengers</td>\n",
+       "      <td>1961-05-01</td>\n",
+       "      <td>507.442200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>AirPassengers</td>\n",
+       "      <td>1961-06-01</td>\n",
+       "      <td>591.439697</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>AirPassengers</td>\n",
+       "      <td>1961-07-01</td>\n",
+       "      <td>677.219604</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>AirPassengers</td>\n",
+       "      <td>1961-08-01</td>\n",
+       "      <td>671.642700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>AirPassengers</td>\n",
+       "      <td>1961-09-01</td>\n",
+       "      <td>575.945312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>AirPassengers</td>\n",
+       "      <td>1961-10-01</td>\n",
+       "      <td>489.476593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>AirPassengers</td>\n",
+       "      <td>1961-11-01</td>\n",
+       "      <td>410.326599</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>AirPassengers</td>\n",
+       "      <td>1961-12-01</td>\n",
+       "      <td>458.730621</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>AirPassengers</td>\n",
+       "      <td>1962-01-01</td>\n",
+       "      <td>495.493530</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>AirPassengers</td>\n",
+       "      <td>1962-02-01</td>\n",
+       "      <td>462.354187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>AirPassengers</td>\n",
+       "      <td>1962-03-01</td>\n",
+       "      <td>526.463745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>AirPassengers</td>\n",
+       "      <td>1962-04-01</td>\n",
+       "      <td>540.006958</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>AirPassengers</td>\n",
+       "      <td>1962-05-01</td>\n",
+       "      <td>578.350037</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>AirPassengers</td>\n",
+       "      <td>1962-06-01</td>\n",
+       "      <td>670.464478</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>AirPassengers</td>\n",
+       "      <td>1962-07-01</td>\n",
+       "      <td>765.478027</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>AirPassengers</td>\n",
+       "      <td>1962-08-01</td>\n",
+       "      <td>778.464722</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>AirPassengers</td>\n",
+       "      <td>1962-09-01</td>\n",
+       "      <td>655.081909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>AirPassengers</td>\n",
+       "      <td>1962-10-01</td>\n",
+       "      <td>560.256226</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>AirPassengers</td>\n",
+       "      <td>1962-11-01</td>\n",
+       "      <td>487.871765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>AirPassengers</td>\n",
+       "      <td>1962-12-01</td>\n",
+       "      <td>535.556213</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        unique_id         ds  PatchTST-FM\n",
+       "0   AirPassengers 1961-01-01   455.334595\n",
+       "1   AirPassengers 1961-02-01   426.086121\n",
+       "2   AirPassengers 1961-03-01   480.885254\n",
+       "3   AirPassengers 1961-04-01   490.008148\n",
+       "4   AirPassengers 1961-05-01   507.442200\n",
+       "5   AirPassengers 1961-06-01   591.439697\n",
+       "6   AirPassengers 1961-07-01   677.219604\n",
+       "7   AirPassengers 1961-08-01   671.642700\n",
+       "8   AirPassengers 1961-09-01   575.945312\n",
+       "9   AirPassengers 1961-10-01   489.476593\n",
+       "10  AirPassengers 1961-11-01   410.326599\n",
+       "11  AirPassengers 1961-12-01   458.730621\n",
+       "12  AirPassengers 1962-01-01   495.493530\n",
+       "13  AirPassengers 1962-02-01   462.354187\n",
+       "14  AirPassengers 1962-03-01   526.463745\n",
+       "15  AirPassengers 1962-04-01   540.006958\n",
+       "16  AirPassengers 1962-05-01   578.350037\n",
+       "17  AirPassengers 1962-06-01   670.464478\n",
+       "18  AirPassengers 1962-07-01   765.478027\n",
+       "19  AirPassengers 1962-08-01   778.464722\n",
+       "20  AirPassengers 1962-09-01   655.081909\n",
+       "21  AirPassengers 1962-10-01   560.256226\n",
+       "22  AirPassengers 1962-11-01   487.871765\n",
+       "23  AirPassengers 1962-12-01   535.556213"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result.fcst_df"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "timecopilot",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From dd9f94bd0c84833851d90b1fba25af07b18296da Mon Sep 17 00:00:00 2001
From: spolisar <22416070+spolisar@users.noreply.github.com>
Date: Mon, 2 Mar 2026 11:54:02 -0500
Subject: [PATCH 04/17] fix: relative import for patchtst-fm

---
 timecopilot/models/foundation/patchtst_fm.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/timecopilot/models/foundation/patchtst_fm.py b/timecopilot/models/foundation/patchtst_fm.py
index b20cb17..fe27ee2 100644
--- a/timecopilot/models/foundation/patchtst_fm.py
+++ b/timecopilot/models/foundation/patchtst_fm.py
@@ -7,8 +7,8 @@
 from tqdm import tqdm
 from tsfm_public import PatchTSTFMForPrediction
 
-from timecopilot.models.foundation.utils import TimeSeriesDataset
-from timecopilot.models.utils.forecaster import Forecaster, QuantileConverter
+from ..utils.forecaster import Forecaster, QuantileConverter
+from .utils import TimeSeriesDataset
 
 # default to the median quantile
 # PatchTST-FM supports quantiles from 0.01 to 0.99

From 58d8dfe1f2eeb32f35d1b1d49669a94d3ef8d2a1 Mon Sep 17 00:00:00 2001
From: spolisar <22416070+spolisar@users.noreply.github.com>
Date: Wed, 4 Mar 2026 15:47:40 -0500
Subject: [PATCH 05/17] docs: add patchtst-fm notebook to mkdocs

---
 mkdocs.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/mkdocs.yml b/mkdocs.yml
index 0c84b22..f4df3da 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -30,6 +30,7 @@ nav:
           - examples/chronos-family.ipynb
           - examples/cryptocurrency-quickstart.ipynb
           - examples/sktime.ipynb
+          - examples/patchtst-fm.ipynb
   - Experiments:
       - experiments/gift-eval.md
       - experiments/fev.md

From 42e4684280f283ebc032bc32ba9a8ce73028e3d3 Mon Sep 17 00:00:00 2001
From: spolisar <22416070+spolisar@users.noreply.github.com>
Date: Wed, 4 Mar 2026 20:53:29 -0500
Subject: [PATCH 06/17] test: add patchtst-fm to tests

---
 tests/models/conftest.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/tests/models/conftest.py b/tests/models/conftest.py
index 7d6633e..ab97e62 100644
--- a/tests/models/conftest.py
+++ b/tests/models/conftest.py
@@ -6,6 +6,7 @@
 from timecopilot.models.foundation.chronos import Chronos
 from timecopilot.models.foundation.flowstate import FlowState
 from timecopilot.models.foundation.moirai import Moirai
+from timecopilot.models.foundation.patchtst_fm import PatchTSTFM
 from timecopilot.models.foundation.timesfm import TimesFM
 from timecopilot.models.foundation.toto import Toto
 from timecopilot.models.ml import AutoLGBM
@@ -69,6 +70,9 @@ def disable_mps_session(monkeypatch):
         repo_id="ibm-granite/granite-timeseries-flowstate-r1",
         alias="FlowState-Granite",
     ),
+    PatchTSTFM(
+        context_length=2_048,
+    ),
     Toto(context_length=256, batch_size=2),
     Moirai(
         context_length=256,

From 1a25cbeede6689e7a433529b00b166807c3145f4 Mon Sep 17 00:00:00 2001
From: spolisar <22416070+spolisar@users.noreply.github.com>
Date: Wed, 4 Mar 2026 20:57:59 -0500
Subject: [PATCH 07/17] fix: fix issues in patchtst-fm

- raise exception when forecasting with levels

- handle varying shapes better for mean calculation
---
 tests/models/test_models.py                  |  7 +++++-
 timecopilot/models/foundation/patchtst_fm.py | 24 ++++++++++++--------
 2 files changed, 21 insertions(+), 10 deletions(-)

diff --git a/tests/models/test_models.py b/tests/models/test_models.py
index bf9cb46..78de36a 100644
--- a/tests/models/test_models.py
+++ b/tests/models/test_models.py
@@ -239,7 +239,12 @@ def test_using_quantiles(model):
 def test_using_level(model):
     level = [0, 20, 40, 60, 80]  # corresponds to qs [0.1, 0.2, ..., 0.9]
     df = generate_series(n_series=2, freq="D")
-    if model.alias in ["AutoLGBM", "AutoNHITS", "AutoTFT"]:
+    if model.alias in [
+        "AutoLGBM",
+        "AutoNHITS",
+        "AutoTFT",
+        "PatchTST-FM",
+    ]:
         # AutoLGBM does not support quantiles yet
         with pytest.raises(ValueError) as excinfo:
             model.forecast(
diff --git a/timecopilot/models/foundation/patchtst_fm.py b/timecopilot/models/foundation/patchtst_fm.py
index fe27ee2..25e2be7 100644
--- a/timecopilot/models/foundation/patchtst_fm.py
+++ b/timecopilot/models/foundation/patchtst_fm.py
@@ -32,7 +32,7 @@ def __init__(
         self,
         repo_id: str = "ibm-research/patchtst-fm-r1",
         # scale_factor: float | None = None,
-        context_length: int = 2_048,
+        context_length: int = 8192,  # default from granite-tsfm
         batch_size: int = 2_048,
         alias: str = "PatchTST-FM",
     ):
@@ -82,12 +82,12 @@ def __init__(
         # NOTE: 'mps' may not be 100% reliable, initial tests with the
         # patchtst-fm gift_eval notebook resulted in predictions of 0 across
         # the board. for now use mps when available, change if it becomes an issue.
-        # self.device = "cuda" if torch.cuda.is_available() else "cpu"
-        self.device = (
-            "cuda"
-            if torch.cuda.is_available()
-            else ("mps" if torch.mps.is_available() else "cpu")
-        )
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        # self.device = (
+        #     "cuda"
+        #     if torch.cuda.is_available()
+        #     else ("mps" if torch.mps.is_available() else "cpu")
+        # )
         self.alias = alias
         self.dtype = torch.float32
 
@@ -181,7 +181,7 @@ def _predict_batch(
         # may not be the ideal solution, but this should be more adaptable
         # when quantiles can vary.
         # there is no guarantee that 0.5 will be in the list of quantiles.
-        mean = fcst.mean(dim=2).squeeze()
+        mean = fcst.mean(dim=-1).squeeze() if fcst.ndim >= 3 else fcst.squeeze()
         # fcst_mean = fcst[..., quantile_levels.index(0.5)].squeeze()
         fcst_mean = mean
         fcst_mean_np = fcst_mean.detach().numpy(force=True)
@@ -276,6 +276,11 @@ def forecast(
                 identifiers as the input DataFrame.
         """
         freq = self._maybe_infer_freq(df, freq)
+        # When support for levels is added remove PatchTST-FM
+        # from the list of models that throw this exception in
+        # tests/models/test_models:test_using_level()
+        if level is not None:
+            raise ValueError("Level is not supported for patchtst-fm yet.")
         qc = QuantileConverter(level=level, quantiles=quantiles)
         dataset = TimeSeriesDataset.from_df(
             df,
@@ -293,7 +298,8 @@ def forecast(
                 raise ValueError(
                     "PatchTSTFM only supports the default quantiles, "
                     f"supported quantiles are {supported_quantiles}, "
-                    "please use the default quantiles or default level, "
+                    f"quantiles provided are {qc.quantiles}",
+                    "please use the default quantiles or default level, ",
                 )
 
             fcsts_mean_np, fcsts_quantiles_np = self._predict(

From a7e3432a2b92f2cc86d70a242b78e3777015d77a Mon Sep 17 00:00:00 2001
From: AzulGarza <fede.garza.ramirez@gmail.com>
Date: Thu, 5 Mar 2026 11:28:38 -0800
Subject: [PATCH 08/17] fix: toml sort

---
 uv.lock | 12658 +++++++++++++++++++++++++++---------------------------
 1 file changed, 6329 insertions(+), 6329 deletions(-)

diff --git a/uv.lock b/uv.lock
index f2eb914..06773e7 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,8578 +1,8578 @@
-version = 1
-revision = 3
 requires-python = ">=3.10"
 resolution-markers = [
-    "python_full_version < '3.11' and sys_platform != 'linux'",
-    "python_full_version < '3.11' and sys_platform == 'linux'",
-    "python_full_version == '3.11.*' and sys_platform != 'linux'",
-    "python_full_version == '3.11.*' and sys_platform == 'linux'",
-    "python_full_version == '3.12.*' and sys_platform != 'linux'",
-    "python_full_version == '3.12.*' and sys_platform == 'linux'",
-    "python_full_version == '3.13.*' and sys_platform != 'linux'",
-    "python_full_version == '3.13.*' and sys_platform == 'linux'",
-    "python_full_version >= '3.14' and sys_platform != 'linux'",
-    "python_full_version >= '3.14' and sys_platform == 'linux'",
+  "python_full_version < '3.11' and sys_platform != 'linux'",
+  "python_full_version < '3.11' and sys_platform == 'linux'",
+  "python_full_version == '3.11.*' and sys_platform != 'linux'",
+  "python_full_version == '3.11.*' and sys_platform == 'linux'",
+  "python_full_version == '3.12.*' and sys_platform != 'linux'",
+  "python_full_version == '3.12.*' and sys_platform == 'linux'",
+  "python_full_version == '3.13.*' and sys_platform != 'linux'",
+  "python_full_version == '3.13.*' and sys_platform == 'linux'",
+  "python_full_version >= '3.14' and sys_platform != 'linux'",
+  "python_full_version >= '3.14' and sys_platform == 'linux'",
 ]
+revision = 3
+version = 1
 
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-    { name = "packaging" },
-    { name = "psutil" },
-    { name = "pyyaml" },
-    { name = "safetensors" },
-    { name = "torch" },
+  {marker = "python_full_version < '3.13'", name = "numpy", source = {registry = "https://pypi.org/simple"}, version = "1.26.4"},
+  {marker = "python_full_version >= '3.13'", name = "numpy", source = {registry = "https://pypi.org/simple"}, version = "2.1.3"},
+  {name = "huggingface-hub"},
+  {name = "packaging"},
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+  {name = "safetensors"},
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From fb260be7c5e7ba564301c9ef922647e91fa9f6a3 Mon Sep 17 00:00:00 2001
From: spolisar <22416070+spolisar@users.noreply.github.com>
Date: Thu, 5 Mar 2026 16:05:52 -0500
Subject: [PATCH 09/17] test: update comments on quantile/level support

---
 tests/models/test_models.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/models/test_models.py b/tests/models/test_models.py
index 78de36a..daa7ade 100644
--- a/tests/models/test_models.py
+++ b/tests/models/test_models.py
@@ -194,7 +194,7 @@ def test_using_quantiles(model):
     qs = [round(i * 0.1, 1) for i in range(1, 10)]
     df = generate_series(n_series=3, freq="D")
     if model.alias in ["AutoLGBM", "AutoNHITS", "AutoTFT"]:
-        # AutoLGBM does not support quantiles yet
+        # These models do not support quantiles yet
         with pytest.raises(ValueError) as excinfo:
             model.forecast(
                 df=df,
@@ -245,7 +245,7 @@ def test_using_level(model):
         "AutoTFT",
         "PatchTST-FM",
     ]:
-        # AutoLGBM does not support quantiles yet
+        # These models do not support levels yet
         with pytest.raises(ValueError) as excinfo:
             model.forecast(
                 df=df,

From 4f42e68f6c8088b9c9245cb410d0d7f86f02c438 Mon Sep 17 00:00:00 2001
From: spolisar <22416070+spolisar@users.noreply.github.com>
Date: Thu, 5 Mar 2026 16:08:34 -0500
Subject: [PATCH 10/17] fix: remove unused args in patchtst-fm

---
 timecopilot/models/foundation/patchtst_fm.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/timecopilot/models/foundation/patchtst_fm.py b/timecopilot/models/foundation/patchtst_fm.py
index 25e2be7..8a98b8b 100644
--- a/timecopilot/models/foundation/patchtst_fm.py
+++ b/timecopilot/models/foundation/patchtst_fm.py
@@ -155,7 +155,6 @@ def _predict_batch(
         batch: list[torch.Tensor] | torch.Tensor,
         h: int,
         quantiles: list[float] | None,
-        supported_quantiles: list[float],
         # scale_factor: float,
     ) -> tuple[np.ndarray, np.ndarray | None]:
         context = self._prepare_and_validate_context(batch)
@@ -196,7 +195,6 @@ def _predict(
         dataset: TimeSeriesDataset,
         h: int,
         quantiles: list[float] | None,
-        supported_quantiles: list[float],
         # scale_factor: float,
     ) -> tuple[np.ndarray, np.ndarray | None]:
         fcsts = [
@@ -205,7 +203,6 @@ def _predict(
                 batch,
                 h,
                 quantiles,
-                supported_quantiles,
                 # scale_factor,
             )
             for batch in tqdm(dataset)
@@ -307,7 +304,6 @@ def forecast(
                 dataset,
                 h,
                 quantiles=qc.quantiles,
-                supported_quantiles=supported_quantiles,
                 # scale_factor=scale_factor,
             )
 

From a98d303bd8f8d172605501d4657ee5ab55719095 Mon Sep 17 00:00:00 2001
From: spolisar <22416070+spolisar@users.noreply.github.com>
Date: Thu, 5 Mar 2026 16:16:13 -0500
Subject: [PATCH 11/17] fix: update context_length default in docstring

---
 timecopilot/models/foundation/patchtst_fm.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/timecopilot/models/foundation/patchtst_fm.py b/timecopilot/models/foundation/patchtst_fm.py
index 8a98b8b..8a6b7b3 100644
--- a/timecopilot/models/foundation/patchtst_fm.py
+++ b/timecopilot/models/foundation/patchtst_fm.py
@@ -47,7 +47,7 @@ def __init__(
 
             context_length (int, optional): Maximum context length (input window size)
                 for the model. Controls how much history is used for each forecast.
-                Defaults to 2,048. The model supports flexible context lengths.
+                Defaults to 8,192. The model supports flexible context lengths.
             batch_size (int, optional): Batch size for inference. Defaults to 2,048.
                 Adjust based on available memory and model size. Larger batch sizes
                 can improve throughput but require more GPU memory.

From 81ffb4c27680014b7c936c6f43fb128f199ee2fc Mon Sep 17 00:00:00 2001
From: spolisar <spolisar@users.noreply.github.com>
Date: Thu, 5 Mar 2026 16:19:14 -0500
Subject: [PATCH 12/17] Update quantile error message.

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---
 timecopilot/models/foundation/patchtst_fm.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/timecopilot/models/foundation/patchtst_fm.py b/timecopilot/models/foundation/patchtst_fm.py
index 8a6b7b3..b5bf2e5 100644
--- a/timecopilot/models/foundation/patchtst_fm.py
+++ b/timecopilot/models/foundation/patchtst_fm.py
@@ -295,8 +295,8 @@ def forecast(
                 raise ValueError(
                     "PatchTSTFM only supports the default quantiles, "
                     f"supported quantiles are {supported_quantiles}, "
-                    f"quantiles provided are {qc.quantiles}",
-                    "please use the default quantiles or default level, ",
+                    f"quantiles provided are {qc.quantiles}, "
+                    "please use the default quantiles or default level."
                 )
 
             fcsts_mean_np, fcsts_quantiles_np = self._predict(

From e2d5d0c85a322ebddb6578afaad0962acf9e1f76 Mon Sep 17 00:00:00 2001
From: spolisar <22416070+spolisar@users.noreply.github.com>
Date: Thu, 5 Mar 2026 17:34:01 -0500
Subject: [PATCH 13/17] fix: small reshape change

---
 timecopilot/models/foundation/patchtst_fm.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/timecopilot/models/foundation/patchtst_fm.py b/timecopilot/models/foundation/patchtst_fm.py
index b5bf2e5..f1b237b 100644
--- a/timecopilot/models/foundation/patchtst_fm.py
+++ b/timecopilot/models/foundation/patchtst_fm.py
@@ -307,14 +307,14 @@ def forecast(
                 # scale_factor=scale_factor,
             )
 
-        fcst_df[self.alias] = fcsts_mean_np.reshape(-1, 1)
+        fcst_df[self.alias] = fcsts_mean_np.reshape(-1)
 
         # should only enter when quantiles are used
         if qc.quantiles is not None and fcsts_quantiles_np is not None:
             for i, q in enumerate(qc.quantiles):
                 fcst_df[f"{self.alias}-q-{int(q * 100)}"] = fcsts_quantiles_np[
                     ..., i
-                ].reshape(-1, 1)
+                ].reshape(-1)
             fcst_df = qc.maybe_convert_quantiles_to_level(
                 fcst_df,
                 models=[self.alias],

From 4591536ec181c2ff1947bcf3fd428dd7897dc15f Mon Sep 17 00:00:00 2001
From: spolisar <22416070+spolisar@users.noreply.github.com>
Date: Thu, 5 Mar 2026 17:34:29 -0500
Subject: [PATCH 14/17] docs: update patchtst-fm notebook with plot

---
 docs/examples/patchtst-fm.ipynb | 210 ++++++++++++++++++--------------
 1 file changed, 119 insertions(+), 91 deletions(-)

diff --git a/docs/examples/patchtst-fm.ipynb b/docs/examples/patchtst-fm.ipynb
index 6825929..e5b9a05 100644
--- a/docs/examples/patchtst-fm.ipynb
+++ b/docs/examples/patchtst-fm.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "45ab9d16",
    "metadata": {},
    "outputs": [],
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "8a43f8f9",
    "metadata": {},
    "outputs": [
@@ -38,7 +38,7 @@
    ],
    "source": [
     "import pandas as pd\n",
-    "from timecopilot import TimeCopilot\n",
+    "from timecopilot import TimeCopilot, TimeCopilotForecaster\n",
     "from timecopilot.models.foundation.patchtst_fm import PatchTSTFM"
    ]
   },
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "4ddf583e",
    "metadata": {},
    "outputs": [],
@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "cba895b1",
    "metadata": {},
    "outputs": [],
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "e6bd16a2",
    "metadata": {},
    "outputs": [],
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "5af63b5a",
    "metadata": {},
    "outputs": [
@@ -113,38 +113,39 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:p-33087:t-8427712640:_client.py:_send_single_request:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
-      "0it [00:00, ?it/s]INFO:p-33087:t-8427712640:modeling_patchtst_fm.py:forward:Context Len: 8192 | Forecast Len: 24 \n",
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-      "INFO:p-33087:t-8427712640:_client.py:_send_single_request:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
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-      "INFO:p-33087:t-8427712640:_client.py:_send_single_request:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n"
+      "INFO:p-63174:t-8427712640:_client.py:_send_single_request:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
+      "0it [00:00, ?it/s]INFO:p-63174:t-8427712640:modeling_patchtst_fm.py:forward:Context Len: 8192 | Forecast Len: 24 \n",
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+      "INFO:p-63174:t-8427712640:_client.py:_send_single_request:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
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+      "INFO:p-63174:t-8427712640:_client.py:_send_single_request:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
+      "0it [00:00, ?it/s]INFO:p-63174:t-8427712640:modeling_patchtst_fm.py:forward:Context Len: 8192 | Forecast Len: 12 \n",
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+      "11it [00:06,  1.73it/s]\n",
+      "INFO:p-63174:t-8427712640:_client.py:_send_single_request:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n"
      ]
     }
    ],
@@ -156,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "f1d4acd7",
    "metadata": {},
    "outputs": [
@@ -164,7 +165,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The time series 'AirPassengers' has a series_length of 144 indicating a long enough series for analysis. The high stability value of 0.933 implies consistent behavior over time with a low susceptibility to random fluctuations. Entropy being low at 0.4287 indicates predictability. High autocorrelation factors like x_acf1 (0.948) and seas_acf1 (0.760) corroborate pronounced seasonal patterns, typical in monthly airline passenger data. Holt-Winters parameters suggest significant trend (alpha close to 1) and seasonality (gamma at 0.747) but no trend smoothing (beta=0).\n"
+      "The time series 'AirPassengers' indicates strong seasonality, as evidenced by a seasonal strength of 0.9815. The ACF1 value is high (0.948), reflecting strong autocorrelation which is typical in seasonal series. The unit root KPSS statistic (2.739) suggests non-stationarity. The entropy is low (0.429), indicating low randomness, while STL decomposition identifies a clear seasonal component with a period of 12 (monthly data).\n"
      ]
     }
    ],
@@ -174,7 +175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "5bad75f1",
    "metadata": {},
    "outputs": [
@@ -209,145 +210,145 @@
        "      <th>0</th>\n",
        "      <td>AirPassengers</td>\n",
        "      <td>1961-01-01</td>\n",
-       "      <td>455.334595</td>\n",
+       "      <td>455.369843</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>AirPassengers</td>\n",
        "      <td>1961-02-01</td>\n",
-       "      <td>426.086121</td>\n",
+       "      <td>426.025330</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>AirPassengers</td>\n",
        "      <td>1961-03-01</td>\n",
-       "      <td>480.885254</td>\n",
+       "      <td>480.801575</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>AirPassengers</td>\n",
        "      <td>1961-04-01</td>\n",
-       "      <td>490.008148</td>\n",
+       "      <td>489.973755</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>AirPassengers</td>\n",
        "      <td>1961-05-01</td>\n",
-       "      <td>507.442200</td>\n",
+       "      <td>507.733276</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>AirPassengers</td>\n",
        "      <td>1961-06-01</td>\n",
-       "      <td>591.439697</td>\n",
+       "      <td>591.214844</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>AirPassengers</td>\n",
        "      <td>1961-07-01</td>\n",
-       "      <td>677.219604</td>\n",
+       "      <td>677.264404</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>AirPassengers</td>\n",
        "      <td>1961-08-01</td>\n",
-       "      <td>671.642700</td>\n",
+       "      <td>672.098755</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>AirPassengers</td>\n",
        "      <td>1961-09-01</td>\n",
-       "      <td>575.945312</td>\n",
+       "      <td>576.013306</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>AirPassengers</td>\n",
        "      <td>1961-10-01</td>\n",
-       "      <td>489.476593</td>\n",
+       "      <td>489.467194</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>AirPassengers</td>\n",
        "      <td>1961-11-01</td>\n",
-       "      <td>410.326599</td>\n",
+       "      <td>410.380615</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>AirPassengers</td>\n",
        "      <td>1961-12-01</td>\n",
-       "      <td>458.730621</td>\n",
+       "      <td>458.822571</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>AirPassengers</td>\n",
        "      <td>1962-01-01</td>\n",
-       "      <td>495.493530</td>\n",
+       "      <td>495.677673</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>AirPassengers</td>\n",
        "      <td>1962-02-01</td>\n",
-       "      <td>462.354187</td>\n",
+       "      <td>462.333984</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>AirPassengers</td>\n",
        "      <td>1962-03-01</td>\n",
-       "      <td>526.463745</td>\n",
+       "      <td>526.376099</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>AirPassengers</td>\n",
        "      <td>1962-04-01</td>\n",
-       "      <td>540.006958</td>\n",
+       "      <td>539.923218</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>AirPassengers</td>\n",
        "      <td>1962-05-01</td>\n",
-       "      <td>578.350037</td>\n",
+       "      <td>578.381042</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>AirPassengers</td>\n",
        "      <td>1962-06-01</td>\n",
-       "      <td>670.464478</td>\n",
+       "      <td>670.164917</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>AirPassengers</td>\n",
        "      <td>1962-07-01</td>\n",
-       "      <td>765.478027</td>\n",
+       "      <td>765.694946</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>AirPassengers</td>\n",
        "      <td>1962-08-01</td>\n",
-       "      <td>778.464722</td>\n",
+       "      <td>778.758301</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td>AirPassengers</td>\n",
        "      <td>1962-09-01</td>\n",
-       "      <td>655.081909</td>\n",
+       "      <td>655.340515</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td>AirPassengers</td>\n",
        "      <td>1962-10-01</td>\n",
-       "      <td>560.256226</td>\n",
+       "      <td>560.360107</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td>AirPassengers</td>\n",
        "      <td>1962-11-01</td>\n",
-       "      <td>487.871765</td>\n",
+       "      <td>487.918152</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>AirPassengers</td>\n",
        "      <td>1962-12-01</td>\n",
-       "      <td>535.556213</td>\n",
+       "      <td>535.687012</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -355,33 +356,33 @@
       ],
       "text/plain": [
        "        unique_id         ds  PatchTST-FM\n",
-       "0   AirPassengers 1961-01-01   455.334595\n",
-       "1   AirPassengers 1961-02-01   426.086121\n",
-       "2   AirPassengers 1961-03-01   480.885254\n",
-       "3   AirPassengers 1961-04-01   490.008148\n",
-       "4   AirPassengers 1961-05-01   507.442200\n",
-       "5   AirPassengers 1961-06-01   591.439697\n",
-       "6   AirPassengers 1961-07-01   677.219604\n",
-       "7   AirPassengers 1961-08-01   671.642700\n",
-       "8   AirPassengers 1961-09-01   575.945312\n",
-       "9   AirPassengers 1961-10-01   489.476593\n",
-       "10  AirPassengers 1961-11-01   410.326599\n",
-       "11  AirPassengers 1961-12-01   458.730621\n",
-       "12  AirPassengers 1962-01-01   495.493530\n",
-       "13  AirPassengers 1962-02-01   462.354187\n",
-       "14  AirPassengers 1962-03-01   526.463745\n",
-       "15  AirPassengers 1962-04-01   540.006958\n",
-       "16  AirPassengers 1962-05-01   578.350037\n",
-       "17  AirPassengers 1962-06-01   670.464478\n",
-       "18  AirPassengers 1962-07-01   765.478027\n",
-       "19  AirPassengers 1962-08-01   778.464722\n",
-       "20  AirPassengers 1962-09-01   655.081909\n",
-       "21  AirPassengers 1962-10-01   560.256226\n",
-       "22  AirPassengers 1962-11-01   487.871765\n",
-       "23  AirPassengers 1962-12-01   535.556213"
+       "0   AirPassengers 1961-01-01   455.369843\n",
+       "1   AirPassengers 1961-02-01   426.025330\n",
+       "2   AirPassengers 1961-03-01   480.801575\n",
+       "3   AirPassengers 1961-04-01   489.973755\n",
+       "4   AirPassengers 1961-05-01   507.733276\n",
+       "5   AirPassengers 1961-06-01   591.214844\n",
+       "6   AirPassengers 1961-07-01   677.264404\n",
+       "7   AirPassengers 1961-08-01   672.098755\n",
+       "8   AirPassengers 1961-09-01   576.013306\n",
+       "9   AirPassengers 1961-10-01   489.467194\n",
+       "10  AirPassengers 1961-11-01   410.380615\n",
+       "11  AirPassengers 1961-12-01   458.822571\n",
+       "12  AirPassengers 1962-01-01   495.677673\n",
+       "13  AirPassengers 1962-02-01   462.333984\n",
+       "14  AirPassengers 1962-03-01   526.376099\n",
+       "15  AirPassengers 1962-04-01   539.923218\n",
+       "16  AirPassengers 1962-05-01   578.381042\n",
+       "17  AirPassengers 1962-06-01   670.164917\n",
+       "18  AirPassengers 1962-07-01   765.694946\n",
+       "19  AirPassengers 1962-08-01   778.758301\n",
+       "20  AirPassengers 1962-09-01   655.340515\n",
+       "21  AirPassengers 1962-10-01   560.360107\n",
+       "22  AirPassengers 1962-11-01   487.918152\n",
+       "23  AirPassengers 1962-12-01   535.687012"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -389,6 +390,33 @@
    "source": [
     "result.fcst_df"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a0bdb36f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x350 with 1 Axes>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tcf = TimeCopilotForecaster(\n",
+    "    models=[\n",
+    "        model\n",
+    "    ]\n",
+    ")\n",
+    "tcf.plot(df, result.fcst_df)"
+   ]
   }
  ],
  "metadata": {

From a9c8aab2a7d31aad891a0f7ba18de20dbace83d5 Mon Sep 17 00:00:00 2001
From: spolisar <22416070+spolisar@users.noreply.github.com>
Date: Thu, 5 Mar 2026 18:28:07 -0500
Subject: [PATCH 15/17] docs: fix docstring for patchtst-fm class

---
 timecopilot/models/foundation/patchtst_fm.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/timecopilot/models/foundation/patchtst_fm.py b/timecopilot/models/foundation/patchtst_fm.py
index f1b237b..70a2d12 100644
--- a/timecopilot/models/foundation/patchtst_fm.py
+++ b/timecopilot/models/foundation/patchtst_fm.py
@@ -17,14 +17,14 @@
 
 class PatchTSTFM(Forecaster):
     """
-    FlowState is the first time-scale adjustable Time Series Foundation Model (TSFM),
-    open-sourced by IBM Research. Combining a State Space Model (SSM) Encoder with a
-    Functional Basis Decoder allows FlowState to transition into a timescale invariant
-    coefficient space and make a continuous forecast from this space. This allows
-    FlowState to seamlessly adjust to all possible sampling rates.
+    PatchTST-FM is a Time Series Foundation Model (TSFM) from IBM Research based on a
+    standard patch Transformer. This generic architecture achieves state-of-the-art
+    zero-shot forecasting performance with a straightforward training protocol. The
+    work provides a transparent, reproducible baseline with comprehensive ablations
+    on model scaling, data composition, and training techniques.
 
     See the [official repo](https://github.com/ibm-granite/granite-tsfm) and
-    [paper](https://arxiv.org/abs/2508.05287) for more details.
+    [paper](https://arxiv.org/abs/2602.06909) for more details.
     """
 
     # NOTE: may want to adjust default context_length, default on granite_tsfm is 8192

From db20d39d8d366fd94a414e92d7d4c6d1b86342c5 Mon Sep 17 00:00:00 2001
From: spolisar <22416070+spolisar@users.noreply.github.com>
Date: Thu, 5 Mar 2026 19:14:27 -0500
Subject: [PATCH 16/17] feat: consolidate common data processing actions into
 new class

---
 timecopilot/models/foundation/flowstate.py   | 51 ++---------------
 timecopilot/models/foundation/patchtst_fm.py | 60 +++-----------------
 timecopilot/models/foundation/sundial.py     | 55 ++----------------
 timecopilot/models/utils/forecaster.py       | 53 +++++++++++++++++
 4 files changed, 70 insertions(+), 149 deletions(-)

diff --git a/timecopilot/models/foundation/flowstate.py b/timecopilot/models/foundation/flowstate.py
index 5cb2b87..1e96a5b 100644
--- a/timecopilot/models/foundation/flowstate.py
+++ b/timecopilot/models/foundation/flowstate.py
@@ -3,12 +3,11 @@
 import numpy as np
 import pandas as pd
 import torch
-from gluonts.transform import LastValueImputation
 from tqdm import tqdm
 from tsfm_public import FlowStateForPrediction
 from tsfm_public.models.flowstate.utils.utils import get_fixed_factor
 
-from ..utils.forecaster import Forecaster, QuantileConverter
+from ..utils.forecaster import Forecaster, QuantileConverter, _DataProcessor
 from .utils import TimeSeriesDataset
 
 
@@ -99,49 +98,6 @@ def _get_model(self) -> FlowStateForPrediction:
             del model
             torch.cuda.empty_cache()
 
-    def _left_pad_and_stack_1D(self, tensors: list[torch.Tensor]) -> torch.Tensor:
-        max_len = max(len(c) for c in tensors)
-        padded = []
-        for c in tensors:
-            assert isinstance(c, torch.Tensor)
-            assert c.ndim == 1
-            padding = torch.full(
-                size=(max_len - len(c),),
-                fill_value=torch.nan,
-                device=c.device,
-                dtype=c.dtype,
-            )
-            padded.append(torch.concat((padding, c), dim=-1))
-        return torch.stack(padded)
-
-    def _prepare_and_validate_context(
-        self,
-        context: list[torch.Tensor] | torch.Tensor,
-    ) -> torch.Tensor:
-        if isinstance(context, list):
-            context = self._left_pad_and_stack_1D(context)
-        assert isinstance(context, torch.Tensor)
-        if context.ndim == 1:
-            context = context.unsqueeze(0)
-        assert context.ndim == 2
-        return context
-
-    def _maybe_impute_missing(self, batch: torch.Tensor) -> torch.Tensor:
-        if torch.isnan(batch).any():
-            batch = batch.float().numpy()
-            imputed_rows = []
-            for i in range(batch.shape[0]):
-                row = batch[i]
-                imputed_row = LastValueImputation()(row)
-                imputed_rows.append(imputed_row)
-            batch = np.vstack(imputed_rows)
-            batch = torch.tensor(
-                batch,
-                dtype=self.dtype,
-                device=self.device,
-            )
-        return batch
-
     def _predict_batch(
         self,
         model: FlowStateForPrediction,
@@ -151,10 +107,11 @@ def _predict_batch(
         supported_quantiles: list[float],
         scale_factor: float,
     ) -> tuple[np.ndarray, np.ndarray | None]:
-        context = self._prepare_and_validate_context(batch)
+        data_processor = _DataProcessor(dtype=self.dtype, device=self.device)
+        context = data_processor._prepare_and_validate_context(batch)
         if context.shape[1] > self.context_length:
             context = context[..., -self.context_length :]
-        context = self._maybe_impute_missing(context)
+        context = data_processor._maybe_impute_missing(context)
         # context is (batch, context_length)
         # then we convert it to (context_length, batch, 1)
         context = context.unsqueeze(-1).transpose(0, 1)
diff --git a/timecopilot/models/foundation/patchtst_fm.py b/timecopilot/models/foundation/patchtst_fm.py
index 70a2d12..05c83ac 100644
--- a/timecopilot/models/foundation/patchtst_fm.py
+++ b/timecopilot/models/foundation/patchtst_fm.py
@@ -3,11 +3,10 @@
 import numpy as np
 import pandas as pd
 import torch
-from gluonts.transform import LastValueImputation
 from tqdm import tqdm
 from tsfm_public import PatchTSTFMForPrediction
 
-from ..utils.forecaster import Forecaster, QuantileConverter
+from ..utils.forecaster import Forecaster, QuantileConverter, _DataProcessor
 from .utils import TimeSeriesDataset
 
 # default to the median quantile
@@ -104,51 +103,6 @@ def _get_model(self) -> PatchTSTFMForPrediction:
             elif self.device.startswith("mps"):
                 torch.mps.empty_cache()
 
-    def _left_pad_and_stack_1D(self, tensors: list[torch.Tensor]) -> torch.Tensor:
-        max_len = max(len(c) for c in tensors)
-        padded = []
-        for c in tensors:
-            assert isinstance(c, torch.Tensor)
-            assert c.ndim == 1
-            padding = torch.full(
-                size=(max_len - len(c),),
-                fill_value=torch.nan,
-                device=c.device,
-                dtype=c.dtype,
-            )
-            padded.append(torch.concat((padding, c), dim=-1))
-        return torch.stack(padded)
-
-    def _prepare_and_validate_context(
-        self,
-        context: list[torch.Tensor] | torch.Tensor,
-    ) -> torch.Tensor:
-        if isinstance(context, list):
-            context = self._left_pad_and_stack_1D(context)
-        assert isinstance(context, torch.Tensor)
-        if context.ndim == 1:
-            context = context.unsqueeze(0)
-        assert context.ndim == 2
-        return context
-
-    def _maybe_impute_missing(
-        self, batch: torch.Tensor, dtype=torch.float32
-    ) -> torch.Tensor:
-        if torch.isnan(batch).any():
-            batch = batch.to(dtype=dtype).numpy(force=True)
-            imputed_rows = []
-            for i in range(batch.shape[0]):
-                row = batch[i]
-                imputed_row = LastValueImputation()(row)
-                imputed_rows.append(imputed_row)
-            batch = np.vstack(imputed_rows)
-            batch = torch.tensor(
-                batch,
-                dtype=self.dtype,
-                device=self.device,
-            )
-        return batch
-
     def _predict_batch(
         self,
         model: PatchTSTFMForPrediction,
@@ -157,10 +111,11 @@ def _predict_batch(
         quantiles: list[float] | None,
         # scale_factor: float,
     ) -> tuple[np.ndarray, np.ndarray | None]:
-        context = self._prepare_and_validate_context(batch)
+        data_processor = _DataProcessor(dtype=self.dtype, device=self.device)
+        context = data_processor._prepare_and_validate_context(batch)
         if context.shape[1] > self.context_length:
             context = context[..., -self.context_length :]
-        context = self._maybe_impute_missing(context)
+        context = data_processor._maybe_impute_missing(context)
         # context is (batch, context_length)
 
         # input data is grouped by id
@@ -180,12 +135,11 @@ def _predict_batch(
         # may not be the ideal solution, but this should be more adaptable
         # when quantiles can vary.
         # there is no guarantee that 0.5 will be in the list of quantiles.
-        mean = fcst.mean(dim=-1).squeeze() if fcst.ndim >= 3 else fcst.squeeze()
+        fcst_mean = fcst.mean(dim=-1).squeeze() if fcst.ndim >= 3 else fcst.squeeze()
         # fcst_mean = fcst[..., quantile_levels.index(0.5)].squeeze()
-        fcst_mean = mean
-        fcst_mean_np = fcst_mean.detach().numpy(force=True)
+        fcst_mean_np = fcst_mean.detach().cpu().numpy()
         fcst_quantiles_np = (
-            fcst.detach().numpy(force=True) if quantiles is not None else None
+            fcst.detach().cpu().numpy() if quantiles is not None else None
         )
         return fcst_mean_np, fcst_quantiles_np
 
diff --git a/timecopilot/models/foundation/sundial.py b/timecopilot/models/foundation/sundial.py
index 3ee6a72..ae232ec 100644
--- a/timecopilot/models/foundation/sundial.py
+++ b/timecopilot/models/foundation/sundial.py
@@ -1,17 +1,17 @@
 import sys
 from contextlib import contextmanager
+from typing import TYPE_CHECKING
 
-if sys.version_info >= (3, 13):
+if sys.version_info >= (3, 13) and not TYPE_CHECKING:
     raise ImportError("Sundial requires Python < 3.13")
 
 import numpy as np
 import pandas as pd
 import torch
-from gluonts.transform import LastValueImputation
 from tqdm import tqdm
 from transformers import AutoModelForCausalLM
 
-from ..utils.forecaster import Forecaster, QuantileConverter
+from ..utils.forecaster import Forecaster, QuantileConverter, _DataProcessor
 from .utils import TimeSeriesDataset
 
 
@@ -91,50 +91,6 @@ def _get_model(self) -> AutoModelForCausalLM:
             del model
             torch.cuda.empty_cache()
 
-    def _left_pad_and_stack_1D(self, tensors: list[torch.Tensor]) -> torch.Tensor:
-        max_len = max(len(c) for c in tensors)
-        padded = []
-        for c in tensors:
-            assert isinstance(c, torch.Tensor)
-            assert c.ndim == 1
-            padding = torch.full(
-                size=(max_len - len(c),),
-                fill_value=torch.nan,
-                device=c.device,
-                dtype=c.dtype,
-            )
-            padded.append(torch.concat((padding, c), dim=-1))
-        return torch.stack(padded)
-
-    def _prepare_and_validate_context(
-        self,
-        context: list[torch.Tensor] | torch.Tensor,
-    ) -> torch.Tensor:
-        if isinstance(context, list):
-            context = self._left_pad_and_stack_1D(context)
-        assert isinstance(context, torch.Tensor)
-        if context.ndim == 1:
-            context = context.unsqueeze(0)
-        assert context.ndim == 2
-
-        return context
-
-    def _maybe_impute_missing(self, batch: torch.Tensor) -> torch.Tensor:
-        if torch.isnan(batch).any():
-            batch = batch.float().numpy()
-            imputed_rows = []
-            for i in range(batch.shape[0]):
-                row = batch[i]
-                imputed_row = LastValueImputation()(row)
-                imputed_rows.append(imputed_row)
-            batch = np.vstack(imputed_rows)
-            batch = torch.tensor(
-                batch,
-                dtype=self.dtype,
-                device=self.device,
-            )
-        return batch
-
     def _predict_batch(
         self,
         model: AutoModelForCausalLM,
@@ -142,10 +98,11 @@ def _predict_batch(
         h: int,
         quantiles: list[float] | None,
     ) -> tuple[np.ndarray, np.ndarray | None]:
-        context = self._prepare_and_validate_context(batch)
+        data_processor = _DataProcessor(dtype=self.dtype, device=self.device)
+        context = data_processor._prepare_and_validate_context(batch)
         if context.shape[1] > self.context_length:
             context = context[..., -self.context_length :]
-        context = self._maybe_impute_missing(context)
+        context = data_processor._maybe_impute_missing(context)
         context = context.to(self.device)
         with torch.autocast(device_type=self.device, dtype=self.dtype):
             # (batch_size, num_samples, h)
diff --git a/timecopilot/models/utils/forecaster.py b/timecopilot/models/utils/forecaster.py
index 42322bb..276e785 100644
--- a/timecopilot/models/utils/forecaster.py
+++ b/timecopilot/models/utils/forecaster.py
@@ -2,6 +2,7 @@
 import numpy as np
 import pandas as pd
 import plotly.graph_objects
+import torch
 import utilsforecast.processing as ufp
 from gluonts.time_feature.seasonality import (
     DEFAULT_SEASONALITIES,
@@ -9,6 +10,7 @@
 from gluonts.time_feature.seasonality import (
     get_seasonality as _get_seasonality,
 )
+from gluonts.transform import LastValueImputation
 from prophet import Prophet as ProphetBase
 from scipy import stats
 from tqdm import tqdm
@@ -614,3 +616,54 @@ def maybe_convert_quantiles_to_level(
                     df = ufp.assign_columns(df, hi_tgt, df[hi_src])
                     out_cols.extend([lo_tgt, hi_tgt])
         return df[out_cols]
+
+
+class _DataProcessor:
+    def __init__(self, dtype: torch.dtype, device: torch.device) -> None:
+        self.dtype = dtype
+        self.device = device
+
+    def _left_pad_and_stack_1D(self, tensors: list[torch.Tensor]) -> torch.Tensor:
+        max_len = max(len(c) for c in tensors)
+        padded = []
+        for c in tensors:
+            assert isinstance(c, torch.Tensor)
+            assert c.ndim == 1
+            padding = torch.full(
+                size=(max_len - len(c),),
+                fill_value=torch.nan,
+                device=c.device,
+                dtype=c.dtype,
+            )
+            padded.append(torch.concat((padding, c), dim=-1))
+        return torch.stack(padded)
+
+    def _prepare_and_validate_context(
+        self,
+        context: list[torch.Tensor] | torch.Tensor,
+    ) -> torch.Tensor:
+        if isinstance(context, list):
+            context = self._left_pad_and_stack_1D(context)
+        assert isinstance(context, torch.Tensor)
+        if context.ndim == 1:
+            context = context.unsqueeze(0)
+        assert context.ndim == 2
+        return context
+
+    def _maybe_impute_missing(
+        self, batch: torch.Tensor, dtype=torch.float32
+    ) -> torch.Tensor:
+        if torch.isnan(batch).any():
+            batch = batch.to(dtype=dtype).detach().cpu().numpy()
+            imputed_rows = []
+            for i in range(batch.shape[0]):
+                row = batch[i]
+                imputed_row = LastValueImputation()(row)
+                imputed_rows.append(imputed_row)
+            batch = np.vstack(imputed_rows)
+            batch = torch.tensor(
+                batch,
+                dtype=self.dtype,
+                device=self.device,
+            )
+        return batch

From 001d4f3d92028d5156c5463d550b9fa756d9e618 Mon Sep 17 00:00:00 2001
From: spolisar <22416070+spolisar@users.noreply.github.com>
Date: Fri, 6 Mar 2026 17:40:04 -0500
Subject: [PATCH 17/17] feat: switch to inheriting _DataProcessor

---
 timecopilot/models/foundation/flowstate.py   | 7 +++----
 timecopilot/models/foundation/patchtst_fm.py | 7 +++----
 timecopilot/models/foundation/sundial.py     | 7 +++----
 3 files changed, 9 insertions(+), 12 deletions(-)

diff --git a/timecopilot/models/foundation/flowstate.py b/timecopilot/models/foundation/flowstate.py
index 1e96a5b..81f007c 100644
--- a/timecopilot/models/foundation/flowstate.py
+++ b/timecopilot/models/foundation/flowstate.py
@@ -11,7 +11,7 @@
 from .utils import TimeSeriesDataset
 
 
-class FlowState(Forecaster):
+class FlowState(Forecaster, _DataProcessor):
     """
     FlowState is the first time-scale adjustable Time Series Foundation Model (TSFM),
     open-sourced by IBM Research. Combining a State Space Model (SSM) Encoder with a
@@ -107,11 +107,10 @@ def _predict_batch(
         supported_quantiles: list[float],
         scale_factor: float,
     ) -> tuple[np.ndarray, np.ndarray | None]:
-        data_processor = _DataProcessor(dtype=self.dtype, device=self.device)
-        context = data_processor._prepare_and_validate_context(batch)
+        context = self._prepare_and_validate_context(batch)
         if context.shape[1] > self.context_length:
             context = context[..., -self.context_length :]
-        context = data_processor._maybe_impute_missing(context)
+        context = self._maybe_impute_missing(context)
         # context is (batch, context_length)
         # then we convert it to (context_length, batch, 1)
         context = context.unsqueeze(-1).transpose(0, 1)
diff --git a/timecopilot/models/foundation/patchtst_fm.py b/timecopilot/models/foundation/patchtst_fm.py
index 05c83ac..744b39f 100644
--- a/timecopilot/models/foundation/patchtst_fm.py
+++ b/timecopilot/models/foundation/patchtst_fm.py
@@ -14,7 +14,7 @@
 DEFAULT_QUANTILES = [0.5]
 
 
-class PatchTSTFM(Forecaster):
+class PatchTSTFM(Forecaster, _DataProcessor):
     """
     PatchTST-FM is a Time Series Foundation Model (TSFM) from IBM Research based on a
     standard patch Transformer. This generic architecture achieves state-of-the-art
@@ -111,11 +111,10 @@ def _predict_batch(
         quantiles: list[float] | None,
         # scale_factor: float,
     ) -> tuple[np.ndarray, np.ndarray | None]:
-        data_processor = _DataProcessor(dtype=self.dtype, device=self.device)
-        context = data_processor._prepare_and_validate_context(batch)
+        context = self._prepare_and_validate_context(batch)
         if context.shape[1] > self.context_length:
             context = context[..., -self.context_length :]
-        context = data_processor._maybe_impute_missing(context)
+        context = self._maybe_impute_missing(context)
         # context is (batch, context_length)
 
         # input data is grouped by id
diff --git a/timecopilot/models/foundation/sundial.py b/timecopilot/models/foundation/sundial.py
index ae232ec..daf0baf 100644
--- a/timecopilot/models/foundation/sundial.py
+++ b/timecopilot/models/foundation/sundial.py
@@ -15,7 +15,7 @@
 from .utils import TimeSeriesDataset
 
 
-class Sundial(Forecaster):
+class Sundial(Forecaster, _DataProcessor):
     """
     Sundial is a family of generative time series foundation models,
     pre-trained on TimeBench (10^12 time points). It uses the TimeFlow Loss to
@@ -98,11 +98,10 @@ def _predict_batch(
         h: int,
         quantiles: list[float] | None,
     ) -> tuple[np.ndarray, np.ndarray | None]:
-        data_processor = _DataProcessor(dtype=self.dtype, device=self.device)
-        context = data_processor._prepare_and_validate_context(batch)
+        context = self._prepare_and_validate_context(batch)
         if context.shape[1] > self.context_length:
             context = context[..., -self.context_length :]
-        context = data_processor._maybe_impute_missing(context)
+        context = self._maybe_impute_missing(context)
         context = context.to(self.device)
         with torch.autocast(device_type=self.device, dtype=self.dtype):
             # (batch_size, num_samples, h)