Release 0.13.0 (#537)

* update CHANGELOG

* Update dependencies

* bump u8darts

* Update README

Author: hrzn

CHANGELOG.md

@@ -4,7 +4,47 @@
 Darts is still in an early development phase and we cannot always guarantee backwards compatibility. Changes that may **break code which uses a previous release of Darts** are marked with a "🔴".
 
 ## [Unreleased](https://github.com/unit8co/darts/tree/master)
-[Full Changelog](https://github.com/unit8co/darts/compare/0.12.0...master)
+[Full Changelog](https://github.com/unit8co/darts/compare/0.13.0...master)
+
+## [0.13.0](https://github.com/unit8co/darts/tree/0.13.0) (2021-11-07)
+### For users of the library:
+
+**Added**:
+- New forecasting model: [Temporal Fusion Transformer](https://arxiv.org/abs/1912.09363) (`TFTModel`). 
+  A new deep learning model supporting both past and future covariates.
+- Improved support for Facebook Prophet model (`Prophet`):
+    - Added support for fit & predict with future covariates. For instance:
+      `model.fit(train, future_covariates=train_covariates)` and
+      `model.predict(n=len(test), num_sample=1, future_covariates=test_covariates)`
+    - Added stochastic forecasting, for instance: `model.predict(n=len(test), num_samples=200)`
+    - Added user-defined seasonalities either at model creation with kwarg 
+      `add_seasonality` (`Prophet(add_seasonality=kwargs_dict)`) or pre-fit with 
+      `model.add_seasonality(kwargs)`. For more information on how to add seasonalities,
+       see the [Prophet docs](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.prophet.html).
+    - Added possibility to predict and return the base model's raw output with `model.predict_raw()`.
+      Note that this returns a pd.DataFrame `pred_df`, which will not be supported for further 
+      processing with the Darts API. But it is possible to access Prophet's methods such as 
+      plots with `model.model.plot_compenents(pred_df)`.
+- New `n_random_samples` in `gridsearch()` method, which allows to specify a number of (random)
+  hyper parameters combinations to be tried, in order mainly to limit the gridsearch time.
+- Improvements in the checkpointing and saving of Torch models.
+    - Now models don't save checkpoints by default anymore. Set `save_checkpoints=True` to enable them.
+    - Models can be manually saved with `YourTorchModel.save_model(file_path)` 
+      (file_path pointing to the .pth.tar file).
+    - Models can be manually loaded with `YourTorchModel.load_model(file_path)` or 
+      the original method `YourTorchModel.load_from_checkpoint()`.
+- New `QuantileRegression` Likelihood class in `darts.utils.likelihood_models`.
+  Allows to apply quantile regression loss, and get probabilistic forecasts on all deep 
+  learning models supporting likelihoods.
+  Used by default in the Temporal Fusion Transformer.
+
+**Fixed:**
+- Some issues with `darts.concatenate()`.
+- Fixed some bugs with `RegressionModel`s applied on multivariate series.
+- An issue with the confidence bounds computation in ACF plot.
+- Added a check for some models that do not support `retrain=False` for `historical_forecasts()`.
+- Small fixes in install instructions.
+- Some rendering issues with bullet points lists in examples.
 
 ## [0.12.0](https://github.com/unit8co/darts/tree/0.12.0) (2021-09-25)
 ### For users of the library:

README.md

@@ -138,7 +138,7 @@ Model | Univariate | Multivariate | Probabilistic | Multiple-series training | P
 `NBEATSModel` | ✅ | ✅ | | ✅ | ✅ | | [N-BEATS paper](https://arxiv.org/abs/1905.10437)
 `TCNModel` | ✅ | ✅ | ✅ | ✅ | ✅ | | [TCN paper](https://arxiv.org/abs/1803.01271), [DeepTCN paper](https://arxiv.org/abs/1906.04397), [blog post](https://medium.com/unit8-machine-learning-publication/temporal-convolutional-networks-and-forecasting-5ce1b6e97ce4)
 `TransformerModel` | ✅ | ✅ | ✅ | ✅ | ✅ | | 
-`TFTModel` | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | [TFT paper](https://arxiv.org/pdf/1912.09363.pdf), [PyTorch Forecasting](https://pytorch-forecasting.readthedocs.io/en/latest/models.html)
+`TFTModel` (Temporal Fusion Transformer) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | [TFT paper](https://arxiv.org/pdf/1912.09363.pdf), [PyTorch Forecasting](https://pytorch-forecasting.readthedocs.io/en/latest/models.html)
 Naive Baselines | ✅ | | | | | |
 
 

requirements/core.txt

@@ -7,7 +7,7 @@ pandas>=1.3.0
 ipython>=7.22.0
 tqdm>=4.60.0
 holidays>=0.11.0
-scikit-learn>=0.24.0,<1.0.0
+scikit-learn>=1.0.1
 filterpy>=1.4.5
 joblib>=1.0.0
 requests>=2.25.0

requirements/torch.txt

@@ -1,2 +1,2 @@
-torch>=1.8.0,<1.9.0
-tensorboard>=2.4.0
+torch>=1.8.0
+tensorboard>=2.4.0

setup_u8darts.py

@@ -29,7 +29,7 @@ def read_requirements(path):
 
 setup(
       name='u8darts',
-      version="0.12.0",
+      version="0.13.0",
       description='A python library for easy manipulation and forecasting of time series.',
       long_description=LONG_DESCRIPTION,
       long_description_content_type="text/markdown",