jmoralez
commented
2 months ago Hey. Can you provide a reproducible example (along with some data)? The following works as expected:
from statsforecast import StatsForecast
from statsforecast.models import AutoARIMA
from utilsforecast.data import generate_series
h = 52
series = generate_series(2, freq="W", min_length=200, max_length=300, n_static_features=2)
X_df = series.groupby('unique_id', observed=True).tail(h).drop(columns='y')
train = series.drop(X_df.index)
sf = StatsForecast(models=[AutoARIMA(season_length=4)], freq="W")
sf.forecast(df=train, h=h, X_df=X_df, level=[95])
sm-ak-r33
Description
from statsforecast.models import AutoARIMA models = [AutoARIMA(season_length=4)] sf = StatsForecast( models=models, freq='W', n_jobs=1, ) horizon = 52 level = [95]
fcst = sf.forecast(df=input_df, h=horizon, X_df=ex_df, level=level) fcst.head()
Why does this code give me an error saying
ValueError Traceback (most recent call last) Cell In[111], line 11 8 horizon = 52 9 level = [95] ---> 11 fcst = sf.forecast(df=input_df, h=horizon, X_df=ex_df, level=level) 12 fcst.head()
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/statsforecast/core.py:1565, in StatsForecast.forecast(self, h, df, X_df, level, fitted, sort_df, prediction_intervals, id_col, time_col, target_col) 1561 raise ValueError( 1562 "You must specify
levelwhen usingprediction_intervals" 1563 ) 1564 if self._is_native(df=df): -> 1565 return super().forecast( 1566 df=df, 1567 h=h, 1568 X_df=X_df, 1569 level=level, 1570 fitted=fitted, 1571 sort_df=sort_df, 1572 prediction_intervals=prediction_intervals, 1573 id_col=id_col, 1574 time_col=time_col, 1575 target_col=target_col, 1576 ) 1577 assert df is not None 1578 engine = make_execution_engine(infer_by=[df])File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/statsforecast/core.py:863, in _StatsForecast.forecast(self, h, df, X_df, level, fitted, sort_df, prediction_intervals, id_col, time_col, target_col) 861 X, level = self._parse_X_level(h=h, X=X_df, level=level) 862 if self.n_jobs == 1: --> 863 res_fcsts = self.ga.forecast( 864 models=self.models, 865 h=h, 866 fallback_model=self.fallback_model, 867 fitted=fitted, 868 X=X, 869 level=level, 870 verbose=self.verbose, 871 target_col=target_col, 872 ) 873 else: 874 res_fcsts = self._forecast_parallel( 875 h=h, fitted=fitted, X=X, level=level, target_col=target_col 876 )
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/statsforecast/core.py:209, in GroupedArray.forecast(self, models, h, fallback_model, fitted, X, level, verbose, target_col) 207 if fcsts_i.ndim == 1: 208 fcsts_i = fcsts_i[:, None] --> 209 fcsts[i h : (i + 1) h, cuts[i_model] : cuts[i_model + 1]] = fcsts_i 210 cols += cols_m 211 if fitted:
ValueError: could not broadcast input array from shape (53,3) into shape (52,3)
note that my input dataframe shape is 416 rows × 9 columns, and exogenous dataframe shape is 104 rows × 8 columns.
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