docs

v2/readme.sh

@@ -0,0 +1,26 @@
+wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
+bash Miniconda3-latest-Linux-x86_64.sh 
+conda install mamba -n base -c conda-forge
+# gluonts does not like python 3.12.
+mamba create -n py311 python=3.11
+conda activate py311
+mamba install matplotlib
+mamba install "gluonts[torch]"
+# gluonts does not like pandas 2.2 deprecating capital letter frequency strings. fixed on gluonts dev branch, but not on conda yet.
+mamba install pandas=2.1
+# for neuralforecast
+mamba install -c conda-forge neuralforecast
+# https://github.com/apache/arrow/issues/41058
+mamba install snappy=1.1.10
+
+python test_predictor_statsforecast.py
+# do not use it with AutoARIMA(4*24), that's too slow.
+
+# with EXTENDED_TEST = True, train_df before '2019-02-01', in 1.5 mins:
+MSTL RMSE 30.83026 MAE 23.066748
+SeasonalNaive RMSE 48.91051 MAE 36.31397
+AutoARIMA RMSE 71.8839 MAE 55.241413
+AutoETS RMSE 71.732056 MAE 55.39218
+CES RMSE 80.47097 MAE 70.04802
+DynamicOptimizedTheta RMSE 55.568027 MAE 43.394047
+

v2/test_predictor_statsforecast.py

@@ -110,7 +110,7 @@ models = [
         SeasonalNaive(season_length=seasonality)
 ]
 
-EXTENDED_TEST = False
+EXTENDED_TEST = True
 if EXTENDED_TEST:
     models += [
         # AutoARIMA(season_length=4 * 24) is just too slow, never even finishes,
@@ -127,7 +127,7 @@ freq = '15min'
 sf = StatsForecast(
         models=models,
         freq=freq,
-        n_jobs=1,
+        n_jobs=-1,
 )