mojolearn.STL#

class mojolearn.STL(endog, period=None, seasonal=7, trend=None, low_pass=None, seasonal_deg=1, trend_deg=1, low_pass_deg=1, robust=False, seasonal_jump=1, trend_jump=1, low_pass_jump=1, numeric_mode=None)[source]#

Bases: object

Season-Trend decomposition using Loess.

Splits each series into seasonal, trend and residual components with statsmodels’ STL algorithm and parameters. endog may be one series of shape (n_obs,) or a batch of shape (batch_size, n_obs), one series per row, all sharing the configuration.

Unlike statsmodels, period is required: statsmodels can infer it from a pandas index, which is not supported here.

Parameters:

  • endog (array-like of shape (n_obs,) or (batch_size, n_obs)) – The series, float32. float64 raises TypeError.

  • period (int) – Periodicity of the season, at least 2. Required.

  • seasonal (int, default 7) – Length of the seasonal smoother (odd).

  • trend (int or None, default None) – Length of the trend smoother. None uses statsmodels’ default, the smallest odd integer at least 1.5 * period / (1 - 1.5 / seasonal).

  • low_pass (int or None, default None) – Length of the low-pass filter. None uses the smallest odd integer greater than period.

  • seasonal_deg, trend_deg, low_pass_deg ({0, 1}, default 1) – Degree of each Loess fit (0 constant, 1 linear).

  • robust (bool, default False) – Use robust fitting with outer iterations and robustness weights.

  • seasonal_jump, trend_jump, low_pass_jump (int, default 1) – Evaluate each smoother every jump points and interpolate between.

  • numeric_mode (str or None, default None) – Numeric mode for this model ('identical', 'deterministic' or 'fast'); None uses the process default (see mojolearn.set_numeric_mode()).

Raises:

  • TypeError – If endog is float64.

  • ValueError – If period is missing or below 2, a degree is not 0 or 1, or endog is not 1-D or 2-D.

Examples

>>> import numpy as np, mojolearn
>>> t = np.arange(144, dtype=np.float32)
>>> y = 0.05 * t + np.sin(2 * np.pi * t / 12).astype(np.float32)
>>> res = mojolearn.STL(y, period=12).fit()
>>> res.seasonal.shape
(144,)
__init__(endog, period=None, seasonal=7, trend=None, low_pass=None, seasonal_deg=1, trend_deg=1, low_pass_deg=1, robust=False, seasonal_jump=1, trend_jump=1, low_pass_jump=1, numeric_mode=None)[source]#

Methods

fit([inner_iter, outer_iter])

Run the decomposition.

Attributes

config

The decomposition settings as a dict.
property config#

The decomposition settings as a dict.

fit(inner_iter=None, outer_iter=None)[source]#

Run the decomposition.

Parameters:

  • inner_iter (int or None, default None) – Inner loop iterations. None uses 2 when robust and 5 otherwise, as statsmodels does.

  • outer_iter (int or None, default None) – Outer (robustness) iterations. None uses 15 when robust and 0 otherwise.

Returns:

DecomposeResult – observed, seasonal, trend, resid and weights, each shaped like endog.