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NeuralForecast

 NeuralForecast (models:List[Any], freq:Union[str,int],
                 local_scaler_type:Optional[str]=None)

The core.StatsForecast class allows you to efficiently fit multiple NeuralForecast models for large sets of time series. It operates with pandas DataFrame df that identifies series and datestamps with the unique_id and ds columns. The y column denotes the target time series variable.

TypeDefaultDetails
modelsListInstantiated neuralforecast.models
see collection here.
freqUnionFrequency of the data. Must be a valid pandas or polars offset alias, or an integer.
local_scaler_typeOptionalNoneScaler to apply per-serie to all features before fitting, which is inverted after predicting.
Can be ‘standard’, ‘robust’, ‘robust-iqr’, ‘minmax’ or ‘boxcox’
ReturnsNeuralForecastReturns instantiated NeuralForecast class.
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NeuralForecast.fit

 NeuralForecast.fit (df:Union[pandas.core.frame.DataFrame,polars.dataframe
                     .frame.DataFrame,neuralforecast.compat.SparkDataFrame
                     ,Sequence[str],NoneType]=None, static_df:Union[pandas
                     .core.frame.DataFrame,polars.dataframe.frame.DataFram
                     e,neuralforecast.compat.SparkDataFrame,NoneType]=None
                     , val_size:Optional[int]=0, sort_df:bool=True,
                     use_init_models:bool=False, verbose:bool=False,
                     id_col:str='unique_id', time_col:str='ds',
                     target_col:str='y', distributed_config:Optional[neura
                     lforecast.common._base_model.DistributedConfig]=None,
                     prediction_intervals:Optional[neuralforecast.utils.Pr
                     edictionIntervals]=None)

*Fit the core.NeuralForecast.

Fit models to a large set of time series from DataFrame df. and store fitted models for later inspection.*

TypeDefaultDetails
dfUnionNoneDataFrame with columns [unique_id, ds, y] and exogenous variables.
If None, a previously stored dataset is required.
static_dfUnionNoneDataFrame with columns [unique_id] and static exogenous.
val_sizeOptional0Size of validation set.
sort_dfboolTrueSort df before fitting.
use_init_modelsboolFalseUse initial model passed when NeuralForecast object was instantiated.
verboseboolFalsePrint processing steps.
id_colstrunique_idColumn that identifies each serie.
time_colstrdsColumn that identifies each timestep, its values can be timestamps or integers.
target_colstryColumn that contains the target.
distributed_configOptionalNoneConfiguration to use for DDP training. Currently only spark is supported.
prediction_intervalsOptionalNoneConfiguration to calibrate prediction intervals (Conformal Prediction).
ReturnsNeuralForecastReturns NeuralForecast class with fitted models.
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NeuralForecast.predict

 NeuralForecast.predict (df:Union[pandas.core.frame.DataFrame,polars.dataf
                         rame.frame.DataFrame,neuralforecast.compat.SparkD
                         ataFrame,NoneType]=None, static_df:Union[pandas.c
                         ore.frame.DataFrame,polars.dataframe.frame.DataFr
                         ame,neuralforecast.compat.SparkDataFrame,NoneType
                         ]=None, futr_df:Union[pandas.core.frame.DataFrame
                         ,polars.dataframe.frame.DataFrame,neuralforecast.
                         compat.SparkDataFrame,NoneType]=None,
                         sort_df:bool=True, verbose:bool=False,
                         engine=None,
                         level:Optional[List[Union[int,float]]]=None,
                         **data_kwargs)

*Predict with core.NeuralForecast.

Use stored fitted models to predict large set of time series from DataFrame df.*

TypeDefaultDetails
dfUnionNoneDataFrame with columns [unique_id, ds, y] and exogenous variables.
If a DataFrame is passed, it is used to generate forecasts.
static_dfUnionNoneDataFrame with columns [unique_id] and static exogenous.
futr_dfUnionNoneDataFrame with [unique_id, ds] columns and df’s future exogenous.
sort_dfboolTrueSort df before fitting.
verboseboolFalsePrint processing steps.
engineNoneTypeNoneDistributed engine for inference. Only used if df is a spark dataframe or if fit was called on a spark dataframe.
levelOptionalNoneConfidence levels between 0 and 100.
data_kwargskwargsExtra arguments to be passed to the dataset within each model.
Returnspandas or polars DataFrame**DataFrame with insample models columns for point predictions and probabilistic
predictions for all fitted models. **
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NeuralForecast.cross_validation

 NeuralForecast.cross_validation (df:Union[pandas.core.frame.DataFrame,pol
                                  ars.dataframe.frame.DataFrame,NoneType]=
                                  None, static_df:Union[pandas.core.frame.
                                  DataFrame,polars.dataframe.frame.DataFra
                                  me,NoneType]=None, n_windows:int=1,
                                  step_size:int=1,
                                  val_size:Optional[int]=0,
                                  test_size:Optional[int]=None,
                                  sort_df:bool=True,
                                  use_init_models:bool=False,
                                  verbose:bool=False,
                                  refit:Union[bool,int]=False,
                                  id_col:str='unique_id',
                                  time_col:str='ds', target_col:str='y', p
                                  rediction_intervals:Optional[neuralforec
                                  ast.utils.PredictionIntervals]=None, lev
                                  el:Optional[List[Union[int,float]]]=None
                                  , **data_kwargs)

*Temporal Cross-Validation with core.NeuralForecast.

core.NeuralForecast’s cross-validation efficiently fits a list of NeuralForecast models through multiple windows, in either chained or rolled manner.*

TypeDefaultDetails
dfUnionNoneDataFrame with columns [unique_id, ds, y] and exogenous variables.
If None, a previously stored dataset is required.
static_dfUnionNoneDataFrame with columns [unique_id] and static exogenous.
n_windowsint1Number of windows used for cross validation.
step_sizeint1Step size between each window.
val_sizeOptional0Length of validation size. If passed, set n_windows=None.
test_sizeOptionalNoneLength of test size. If passed, set n_windows=None.
sort_dfboolTrueSort df before fitting.
use_init_modelsboolFalseUse initial model passed when object was instantiated.
verboseboolFalsePrint processing steps.
refitUnionFalseRetrain model for each cross validation window.
If False, the models are trained at the beginning and then used to predict each window.
If positive int, the models are retrained every refit windows.
id_colstrunique_idColumn that identifies each serie.
time_colstrdsColumn that identifies each timestep, its values can be timestamps or integers.
target_colstryColumn that contains the target.
prediction_intervalsOptionalNoneConfiguration to calibrate prediction intervals (Conformal Prediction).
levelOptionalNoneConfidence levels between 0 and 100. Use with prediction_intervals.
data_kwargskwargsExtra arguments to be passed to the dataset within each model.
ReturnsUnion**DataFrame with insample models columns for point predictions and probabilistic
predictions for all fitted models. **
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NeuralForecast.predict_insample

 NeuralForecast.predict_insample (step_size:int=1)

*Predict insample with core.NeuralForecast.

core.NeuralForecast’s predict_insample uses stored fitted models to predict historic values of a time series from the stored dataframe.*

TypeDefaultDetails
step_sizeint1Step size between each window.
Returnspandas.DataFrame**DataFrame with insample predictions for all fitted models. **
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NeuralForecast.save

 NeuralForecast.save (path:str, model_index:Optional[List]=None,
                      save_dataset:bool=True, overwrite:bool=False)

*Save NeuralForecast core class.

core.NeuralForecast’s method to save current status of models, dataset, and configuration. Note that by default the models are not saving training checkpoints to save disk memory, to get them change the individual model **trainer_kwargs to include enable_checkpointing=True.*

TypeDefaultDetails
pathstrDirectory to save current status.
model_indexOptionalNoneList to specify which models from list of self.models to save.
save_datasetboolTrueWhether to save dataset or not.
overwriteboolFalseWhether to overwrite files or not.
/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/fastcore/docscrape.py:230: UserWarning: potentially wrong underline length... 
Parameters 
----------- in 
Load NeuralForecast
...
  else: warn(msg)
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NeuralForecast.load

 NeuralForecast.load (path, verbose=False, **kwargs)

*Load NeuralForecast

core.NeuralForecast’s method to load checkpoint from path.*

TypeDefaultDetails
pathstrDirectory with stored artifacts.
verboseboolFalse
kwargsAdditional keyword arguments to be passed to the function
load_from_checkpoint.
ReturnsNeuralForecastInstantiated NeuralForecast class.
os.environ['NIXTLA_ID_AS_COL'] = '1'

# Test predict_insample step_size

h = 12
train_end = AirPassengersPanel_train['ds'].max()
sizes = AirPassengersPanel_train['unique_id'].value_counts().to_numpy()
for step_size, test_size in [(7, 0), (9, 0), (7, 5), (9, 5)]:
    models = [NHITS(h=h, input_size=12, max_steps=1)]
    nf = NeuralForecast(models=models, freq='M')
    nf.fit(AirPassengersPanel_train)
    # Note: only apply set_test_size() upon nf.fit(), otherwise it would have set the test_size = 0
    nf.models[0].set_test_size(test_size)
    
    forecasts = nf.predict_insample(step_size=step_size)
    last_cutoff = train_end - test_size * pd.offsets.MonthEnd() - h * pd.offsets.MonthEnd()
    n_expected_cutoffs = (sizes[0] - test_size - nf.h + step_size) // step_size

    # compare cutoff values
    expected_cutoffs = np.flip(np.array([last_cutoff - step_size * i * pd.offsets.MonthEnd() for i in range(n_expected_cutoffs)]))
    actual_cutoffs = np.array([pd.Timestamp(x) for x in forecasts[forecasts['unique_id']==nf.uids[1]]['cutoff'].unique()])
    np.testing.assert_array_equal(expected_cutoffs, actual_cutoffs, err_msg=f"{step_size=},{expected_cutoffs=},{actual_cutoffs=}")
    
    # check forecast-points count per series
    cutoffs_by_series = forecasts.groupby(['unique_id', 'cutoff']).size().unstack('unique_id')
    pd.testing.assert_series_equal(cutoffs_by_series['Airline1'], cutoffs_by_series['Airline2'], check_names=False)

def config_optuna(trial):
    return {"input_size": trial.suggest_categorical('input_size', [12, 24]),
        "hist_exog_list": trial.suggest_categorical('hist_exog_list', [['trend'], ['y_[lag12]'], ['trend', 'y_[lag12]']]),
        "futr_exog_list": ['trend'],
        "max_steps": 10,
        "val_check_steps": 5}

config_ray = {'input_size': tune.choice([12, 24]), 
          'hist_exog_list': tune.choice([['trend'], ['y_[lag12]'], ['trend', 'y_[lag12]']]),
          'futr_exog_list': ['trend'],
          'max_steps': 10,
          'val_check_steps': 5}

# Test predict_insample step_size

h = 12
train_end = AirPassengers_pl['time'].max()
sizes = AirPassengers_pl['uid'].value_counts().to_numpy()

for step_size, test_size in [(7, 0), (9, 0), (7, 5), (9, 5)]:
    models = [NHITS(h=h, input_size=12, max_steps=1)]
    nf = NeuralForecast(models=models, freq='1mo')
    nf.fit(
        AirPassengers_pl,
        id_col='uid',
        time_col='time',
        target_col='target',    
    )
    # Note: only apply set_test_size() upon nf.fit(), otherwise it would have set the test_size = 0
    nf.models[0].set_test_size(test_size)    
    
    forecasts = nf.predict_insample(step_size=step_size)
    n_expected_cutoffs = (sizes[0][1] - test_size - nf.h + step_size) // step_size

    # compare cutoff values
    last_cutoff = train_end - test_size * pd.offsets.MonthEnd() - h * pd.offsets.MonthEnd()
    expected_cutoffs = np.flip(np.array([last_cutoff - step_size * i * pd.offsets.MonthEnd() for i in range(n_expected_cutoffs)]))
    pl_cutoffs = forecasts.filter(polars.col('uid') ==nf.uids[1]).select('cutoff').unique(maintain_order=True)
    actual_cutoffs = np.array([pd.Timestamp(x['cutoff']) for x in pl_cutoffs.rows(named=True)])
    np.testing.assert_array_equal(expected_cutoffs, actual_cutoffs, err_msg=f"{step_size=},{expected_cutoffs=},{actual_cutoffs=}")

    # check forecast-points count per series
    cutoffs_by_series = forecasts.group_by(['uid', 'cutoff']).count()
    assert_frame_equal(cutoffs_by_series.filter(polars.col('uid') == "Airline1").select(['cutoff', 'count']), cutoffs_by_series.filter(polars.col('uid') == "Airline2").select(['cutoff', 'count'] ), check_row_order=False)
Predictive MaintenanceAutoformer