hts.core package

hts.core.exceptions

exception hts.core.exceptions.HTSException[source]

Bases: Exception

exception hts.core.exceptions.InvalidArgumentException[source]

Bases: hts.core.exceptions.HTSException

exception hts.core.exceptions.MissingRegressorException[source]

Bases: hts.core.exceptions.HTSException

hts.core.regressor

class hts.core.regressor.HTSRegressor(model: str = 'prophet', revision_method: str = 'OLS', transform: Union[hts._t.Transform, bool, None] = False, n_jobs: int = 1, low_memory: bool = False, **kwargs)[source]

Bases: sklearn.base.BaseEstimator, sklearn.base.RegressorMixin

Main regressor class for scikit-hts. Likely the only import you’ll need for using this project. It takes a pandas dataframe, the nodes specifying the hierarchies, model kind, revision method, and a few other parameters. See Examples to get an idea of how to use it.

Variables:
  • transform (Union[NamedTuple[str, Callable], bool]) – Function transform to be applied to input and outputs. If True, it will use scipy.stats.boxcox and scipy.special._ufuncs.inv_boxcox on input and output data
  • sum_mat (array_like) – The summing matrix, explained in depth in Forecasting
  • nodes (Dict[str, List[str]]) – Nodes representing node, edges of the hierarchy. Keys are nodes, values are list of edges.
  • df (pandas.DataFrame) – The dataframe containing the nodes and edges specified above
  • revision_method (str) – One of: "OLS", "WLSS", "WLSV", "FP", "PHA", "AHP", "BU", "NONE"
  • models (dict) – Dictionary that holds the trained models
  • mse (dict) – Dictionary that holds the mse scores for the trained models
  • residuals (dict) – Dictionary that holds the mse residual for the trained models
  • forecasts (dict) – Dictionary that holds the forecasts for the trained models
  • model_instance (TimeSeriesModel) – Reference to the class implementing the actual time series model
__init__(model: str = 'prophet', revision_method: str = 'OLS', transform: Union[hts._t.Transform, bool, None] = False, n_jobs: int = 1, low_memory: bool = False, **kwargs)[source]
Parameters:
  • model (str) – One of the models supported by hts. These can be found
  • revision_method (str) – The revision method to be used. One of: "OLS", "WLSS", "WLSV", "FP", "PHA", "AHP", "BU", "NONE"
  • transform (Boolean or NamedTuple) –

    If True, scipy.stats.boxcox and scipy.special._ufuncs.inv_boxcox will be applied prior and after fitting. If False (default), no transform is applied. If you desired to use custom functions, use a NamedTuple like:

    from collections import namedtuple

Transform = namedtuple('Transform', ['func', 'inv_func']
transform = Transform(func=numpy.exp, inv_func=numpy.log)

ht = HTSRegressor(transform=transform, ...)

    The signatures for the func as well as inv_func parameters must both be Callable[[numpy.ndarry], numpy.ndarray], i.e. they must take an array and return an array, both of equal dimensions

  • n_jobs (int) – Number of parallel jobs to run the forecasting on
  • low_memory (Bool) – If True, models will be fit, serialized, and released from memory. Usually a good idea if you are dealing with a large amount of nodes
  • kwargs – Keyword arguments to be passed to the underlying model to be instantiated
fit(df: Optional[pandas.core.frame.DataFrame] = None, nodes: Optional[Dict[str, List[str]]] = None, tree: Optional[hts.hierarchy.HierarchyTree] = None, exogenous: Optional[Dict[str, List[str]]] = None, root: str = 'total', distributor: Optional[hts.utilities.distribution.DistributorBaseClass] = None, disable_progressbar=False, show_warnings=False, **fit_kwargs) → hts.core.regressor.HTSRegressor[source]

Fit hierarchical model to dataframe containing hierarchical data as specified in the nodes parameter.

Exogenous can also be passed as a dict of (string, list), where string is the specific node key and the list contains the names of the columns to be used as exogenous variables for that node.

Alternatively, a pre-built HierarchyTree can be passed without specifying the node and df. See more at hts.hierarchy.HierarchyTree

Parameters:
  • df (pandas.DataFrame) – A Dataframe of time series with a DateTimeIndex. Each column represents a node in the hierarchy. Ignored if tree argument is passed
  • nodes (Dict[str, List[str]]) –
    The hierarchy defined as a dict of (string, list), as specified in
    HierarchyTree.from_nodes
  • tree (HierarchyTree) – A pre-built HierarchyTree. Ignored if df and nodes are passed, as the tree will be built from thise
  • distributor (Optional[DistributorBaseClass]) – A distributor, for parallel/distributed processing
  • exogenous (Dict[str, List[str]] or None) – Node key mapping to columns that contain the exogenous variable for that node
  • root (str) – The name of the root node
  • disable_progressbar (Bool) – Disable or enable progressbar
  • show_warnings (Bool) – Disable warnings
  • fit_kwargs (Any) – Any arguments to be passed to the underlying forecasting model’s fit function
Returns:

The fitted HTSRegressor instance
Return type:

HTSRegressor
predict(exogenous_df: pandas.core.frame.DataFrame = None, steps_ahead: int = None, distributor: Optional[hts.utilities.distribution.DistributorBaseClass] = None, disable_progressbar: bool = False, show_warnings: bool = False, **predict_kwargs) → pandas.core.frame.DataFrame[source]
Parameters:
  • distributor (Optional[DistributorBaseClass]) – A distributor, for parallel/distributed processing
  • disable_progressbar (Bool) – Disable or enable progressbar
  • show_warnings (Bool) – Disable warnings
  • predict_kwargs (Any) – Any arguments to be passed to the underlying forecasting model’s predict function
  • exogenous_df (pandas.DataFrame) –

    A dataframe of length == steps_ahead containing the exogenous data for each of the nodes. Only required when using prophet or auto_arima models. See fbprophet’s additional regression docs and AutoARIMA’s exogenous handling docs for more information.

    Other models do not require additional regressors at predict time.

  • steps_ahead (int) – The number of forecasting steps for which to produce a forecast
Returns:

  • Revised Forecasts, as a pandas.DataFrame in the same format as the one passed for fitting, extended by steps_ahead
  • time steps`

hts.core.result

class hts.core.result.HTSResult[source]

Bases: object

errors
forecasts
models
residuals

hts.core.utils