midas

Mixed-data sampling transformer.

A transformer that converts higher frequency time series to lower frequency using mixed-data sampling; see [1]_ for further details. This allows higher frequency covariates to be used whilst forecasting a lower frequency target series. For example, using monthly inputs to forecast a quarterly target.

Notes

The high input frequency should always relate in the same rate to the low target frequency. For example, there's always three months in quarter. However, the number of days in a month varies per month. In the latter case a MIDAS transformation does not work and the transformer will raise an error.

Parameters

low_freq

The pd.DateOffset string alias corresponding to the target low frequency [2]_. Passed on to the rule parameter of pandas.DataFrame.resample().

strip

Whether to remove the NaNs from the start and the end of the transformed series.

drop_static_covariates

If set to True, the statics covariates of the input series won't be transferred to the output. This might be useful for multivariate series with component-specific static covariates.

name

A specific name for the scaler

n_jobs

The number of jobs to run in parallel. Parallel jobs are created only when a Sequence[TimeSeries] is passed as input to a method, parallelizing operations regarding different TimeSeries. Defaults to 1 (sequential). Setting the parameter to -1 means using all the available processors. Note: for a small amount of data, the parallelization overhead could end up increasing the total required amount of time.

verbose

Optionally, whether to print operations progress

Examples

>>> import pandas as pd
>>> from anomx.data.timeseries import TimeSeries
>>> from anomx.data.processing.transformers import MIDAS
>>> monthly_series = TimeSeries.from_times_and_values(
...     pd.date_range("1949-01-01", periods=9, freq="MS", name="Month"),
...     [112., 118., 132., 129., 121., 135., 148., 148., 136.],
... )
>>> print(monthly_series.time_index[:4])
DatetimeIndex(['1949-01-01', '1949-02-01', '1949-03-01', '1949-04-01'], dtype='datetime64[ns]', name='Month', freq='MS')
>>> print(monthly_series.values()[:4])
[[112.]
 [118.]
 [132.]
 [129.]]
>>> midas = MIDAS(low_freq="QS")
>>> quarterly_series = midas.fit_transform(monthly_series)
>>> print(quarterly_series.time_index[:3])
DatetimeIndex(['1949-01-01', '1949-04-01', '1949-07-01'], dtype='datetime64[ns]', name='Month', freq='QS-JAN')
>>> print(quarterly_series.values()[:3])
[[112. 118. 132.]
 [129. 121. 135.]
 [148. 148. 136.]]
>>> inversed_quaterly = midas.inverse_transform(quarterly_series)
>>> print(inversed_quaterly.time_index[:4])
DatetimeIndex(['1949-01-01', '1949-02-01', '1949-03-01', '1949-04-01'], dtype='datetime64[ns]', name='Month', freq='MS')
>>> print(inversed_quaterly.values()[:4])
[[112.]
 [118.]
 [132.]
 [129.]]

References

.. [1] https://en.wikipedia.org/wiki/Mixed-data_sampling .. [2] https://pandas.pydata.org/docs/user_guide/timeseries.html#dateoffset-objects