A Python port of R's stl function
A Python port of R's stl function. Translated rather literally from the original R/Fortran source and vectorized with NumPy.
For more information see the R manual.
Compatible with Python 2 and 3.
pip install --user rstl
Import the STL class and call its constructor.
import numpy as np from rstl import STL ts = np.arange(144) freq = 12 stl = STL(ts, freq, "periodic") trend = stl.trend
The code itself is mostly uncommented due to it being a literal translation.
class STL(ts, freq, s_window, s_degree=0, t_window=None, t_degree=1, l_window=None, l_degree=None, s_jump=None, t_jump=None, l_jump=None, robust=False, inner=None, outer=None):
Note that a value of None means that the default R value will be used. (These could not be specified in the signature because Python does not allow referencing other parameters).
- ts: The time series (numpy array).
- freq: The frequency of the time series.
- s_window: Either the character string "periodic" or the span (in lags) of the loess window for seasonal extraction, which should be odd and at least 7, according to Cleveland et al.
- s_degree, t_window, t_degree, l_window, l_degree, s_jump, t_jump, l_jump, robust, inner and outer. See the R manual for their meanings and defaults.
trend: The trend component of the time series (numpy array).
seasonal: The seasonal component of the time series (numpy array).
remainder: The remainder of the time series not explained by trend and seasonal components (numpy array).
weights: The final robust weights (all one if fitting is not done robustly) (numpy array).
s_window, s_degree, t_window, t_degree, l_window, l_degree, s_jump, t_jump, l_jump, inner and outer. Note that these may have been altered by the program.
According to tests a runtime increase of factor ~3-5 should be expected.
Copyright and License
Python port Copyright 2018 Eric Rauch.
Original source Copyright 2014 B.D. Ripley; Fortran code by Cleveland et al (1990) from ‘netlib’.
Licensed under the GPLv3.
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