compotime.models.LocalLevelForecaster#

class compotime.models.LocalLevelForecaster#

Bases: object

Local level state-space forecaster.

Notes

The local level model is described by the following equations:

\[\begin{split}\boldsymbol y_t &= \boldsymbol l_{t-1} + \boldsymbol \epsilon_t \\ \boldsymbol l_t &= \boldsymbol l_{t-1} + \alpha \boldsymbol \epsilon_t\end{split}\]

where \(\boldsymbol y_t\) represents the unbounded time series observations at timestep \(t\) that result from applying the log-ratio transform, and \(\boldsymbol l_t\) represents the local level.

Equivalently, to express it in the same terms as the LocalTrendForecaster, it is possible to use

\[\begin{split}\boldsymbol y_t' &= \boldsymbol w \boldsymbol x_{t-1} + \boldsymbol \epsilon_t' \\ \boldsymbol x_t &= \boldsymbol F \boldsymbol x_{t-1} + \boldsymbol g \boldsymbol \epsilon_t'\end{split}\]

where

\(\boldsymbol x_t = \boldsymbol l^{'}_{t}\), \(\boldsymbol w = 1\), \(\boldsymbol F = 1\), and \(\boldsymbol g = \alpha\).

__init__()#

Methods

`__init__`()
`fit`(y)	Fit the model.
`predict`(horizon)	Predict future values of the time series.

Attributes

`optim_params_`
`opt_success_`
`X_`
`fitted_curve_`
`colnames_`
`time_idx_`
`idx_freq_`
`base_col_idx_`

fit(y: DataFrame) → Self#

Fit the model.

Parameters:: y – Time series dataframe, where rows represent the timestamps and columns the different shares series.
Returns:: Fitted instance of the model.
Return type:: Self

predict(horizon: int) → DataFrame#

Predict future values of the time series.

Parameters:: horizon – Number of steps into the future to be predicted.
Returns:: Predicted time series.
Return type:: pd.DataFrame