0.3.4

0.3.4

Author: winedarksea

README.md

@@ -53,7 +53,7 @@ Input data is expected to come in either a *long* or a *wide* format:
 - For *long* data, the column name for each of these is passed to .fit() as `date_col`, `id_col`, and `value_col`. No parameters are needed for *wide* data.
 
 ```python
-# also load: _hourly, _daily, _weekly, _yearly, or _live_daily
+# also load: _hourly, _monthly, _weekly, _yearly, or _live_daily
 from autots import AutoTS, load_daily
 
 # sample datasets can be used in either of the long or wide import shapes

TODO.md

@@ -15,21 +15,20 @@
 * Forecasts are desired for the future immediately following the most recent data.
 
 # Latest
-* Fixed horizontal ensembles running in univariate cases (they are explicitly multivariate)
-* 'superfast' transformer list added
-* test on Mac for the first time, everything seems to work except lightgbm
-* include first actual unittests (from existing test.py runs)
-* slight change to random template generation to make sure all models are choosen at least once
-* cleaned up PredictWitch -> model_forecast() a bit so that users can use it to run single models from parameters directly
-* added load_live_daily() example data and spruced up production_example.py
-* tried in vain to make a quiet verbosity option for GluonTS
-* added create_lagged_regressor
-* added Greykite model (additional regressors not working yet)
-* fixed regressors bug in Prophet
-* added a simple plot method to PredictionObject
-* fix for deprecation warning in GLS
+* improvements to joblib parallelized models (not copying the full df)
+* additonal parameter checks
+* made "auto" cpu_count even more conservative
+* improved 'Score' generation. It should now be more equally weighted across metrics.
+* fixed potential bug for horizontal ensemble selection if perfect forecasts were delivered
+* Horizontal ensembles now chosen by combination of multiple metrics and metric_weighting (mae, rmse, spl, contour)
+* re-weighted fillna probabilities for random choice
+* addressed a few deprecation warnings
+* new plot_horizontal() function for AutoTS to quickly visual horizontal ensembles
+* Probabilistic and HDist ensembles are now deprecated (they can still be run by model_forecast but not by AutoTS class)
+* new introduce_na parameter which makes series more robust to the last values being NaN in final but never in any validation
+* Mosaic Ensembles! These can offer major improvements to MAE, but are also less stable than horizontal ensembles.
 
-# Known Errors: 
+# Errors: 
 DynamicFactor holidays 	Exceptions 'numpy.ndarray' object has no attribute 'values'
 VECM does not recognize exog to predict
 ARIMA with User or Holiday ValueError('Can only compare identically-labeled DataFrame objects',)

autots/__init__.py

@@ -19,7 +19,7 @@
 from autots.tools.regressor import create_lagged_regressor
 from autots.evaluator.auto_model import model_forecast
 
-__version__ = '0.3.3'
+__version__ = '0.3.4'
 
 TransformTS = GeneralTransformer
 

autots/datasets/_base.py

@@ -213,7 +213,7 @@ def load_live_daily(
     fred_series: list = ["DGS10", "T5YIE", "SP500", "DCOILWTICO", "DEXUSEU"],
     tickers: list = ["MSFT"],
     trends_list: list = ["forecasting", "cycling", "cpu", "microsoft"],
-    weather_data_types: list = ["AWND", "WSF2"],
+    weather_data_types: list = ["AWND", "WSF2", "TAVG"],
     weather_stations: list = ["USW00094846", "USW00014925"],
     weather_years: int = 10,
     london_air_stations: list = ['CT3', 'SK8'],
@@ -230,7 +230,8 @@ def load_live_daily(
         fred_series (list): list of FRED series IDs. This requires fredapi package
         tickers (list): list of stock tickers, requires yfinance
         trends (list): list of search keywords, requires pytrends.
-        weather_data_types (list): from NCEI NOAA api data types
+        weather_data_types (list): from NCEI NOAA api data types, GHCN Daily Weather Elements
+            PRCP, SNOW, TMAX, TMIN, TAVG, AWND, WSF1, WSF2, WSF5, WSFG
         weather_stations (list): from NCEI NOAA api station ids
         london_air_stations (list): londonair.org.uk source station IDs
         london_species (str): what measurement to pull from London Air. Not all stations have all metrics.\