pyhts

A python package for hierarchical forecasting, inspired by the hts package in R.

Features

• Support pupular forecast reconciliation models in the literature, e.g. ols, wls, mint et al. Forecasting with temporal hierarchies will be supported in the future.
• Multiple methods for the construction of hierarchy.
• Use different base forecasters for different hierarchical levels.
• Sklearn-like API.

Quick Demo

• Load the Australia tourism flows data.

from pyhts._dataset import load_tourism

tourism_data = load_tourism()
train = tourism_data.iloc[:-12, :]
test = tourism_data.iloc[-12:, :]

• Define the hierarchy.

from pyhts._hierarchy import Hierarchy

hierarchy = Hierarchy.from_names(tourism_data.columns, chars=[1, 1, 1])
print(hierarchy.node_name)

• Create an ols forecasting reconciliation model with sklearn-like API.

from pyhts._HFModel import HFModel

model = HFModel(hierarchy=hierarchy, base_forecasters="arima",
                hf_method="comb", comb_method="ols")

• Fit the model and produce forecasts.

model.fit(train)
forecasts = model.predict(horizon=12)

• model.fit() fits the baseforecasters and computes the weighting matrix used to reconcile the base forecasts.

• model.forecast() calculates the base forecasts for all levels and reconciles the base forecasts.

• Obtain coherent forecasts of all the hierarchical levels.

all_level_forecasts = hierarchy.aggregate_ts(forecasts)

• Evaluate the forecasting accuracy.

# accuracy of reconciled forecasts
hierarchy.accuracy(test, forecasts, hist=train, measure=['mase', 'rmse'])

# accuracy of base forecasts
base_forecasts = model.generate_base_forecast(horizon=12)
hierarchy.accuracy_base(test, base_forecasts, hist=train, measure=['mase', 'rmse'])

Because of the incoherence of base forecasts, base_forecasts are forecasts of all time series in the hierarchy, while coherent forecasts are forecasts of the bottom-level time series.

Documentation

See documentation here.