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statsmodels v0.13.0.dev0 (+213) statsmodels.tsa.forecasting.stl.STLForecast
Model-based forecasting using STL to remove seasonality
Forecasts are produced by first subtracting the seasonality estimated using STL, then forecasting the deseasonalized data using a time-series model, for example, ARIMA.
Data to be decomposed. Must be squeezable to 1-d.
The model used to forecast endog after the seasonality has been removed using STL
Any additional arguments needed to initialized the model using the residuals produced by subtracting the seasonality.
Periodicity of the sequence. If None and endog is a pandas Series or DataFrame, attempts to determine from endog. If endog is a ndarray, period must be provided.
Length of the seasonal smoother. Must be an odd integer, and should normally be >= 7 (default).
Length of the trend smoother. Must be an odd integer. If not provided uses the smallest odd integer greater than 1.5 * period / (1 - 1.5 / seasonal), following the suggestion in the original implementation.
Length of the low-pass filter. Must be an odd integer >=3. If not provided, uses the smallest odd integer > period.
Degree of seasonal LOESS. 0 (constant) or 1 (constant and trend).
Degree of trend LOESS. 0 (constant) or 1 (constant and trend).
Degree of low pass LOESS. 0 (constant) or 1 (constant and trend).
Flag indicating whether to use a weighted version that is robust to some forms of outliers.
Positive integer determining the linear interpolation step. If larger than 1, the LOESS is used every seasonal_jump points and linear interpolation is between fitted points. Higher values reduce estimation time.
Positive integer determining the linear interpolation step. If larger than 1, the LOESS is used every trend_jump points and values between the two are linearly interpolated. Higher values reduce estimation time.
Positive integer determining the linear interpolation step. If larger than 1, the LOESS is used every low_pass_jump points and values between the two are linearly interpolated. Higher values reduce estimation time.
Autoregressive modeling supporting complex deterministics.
Additive and multiplicative exponential smoothing with trend.
Additive exponential smoothing with trend.
If
S^t
is the seasonal component, then the deseasonalize series is constructed as
The trend component is not removed, and so the time series model should be capable of adequately fitting and forecasting the trend if present. The out-of-sample forecasts of the seasonal component are produced as
where
k=m−h+m⌊(h−1)/m⌋
tracks the period offset in the full cycle of 1, 2, …, m where m is the period length.
This class is mostly a convenience wrapper around STL and a user-specified model. The model is assumed to follow the standard statsmodels pattern:
fit is used to estimate parameters and returns a results instance, results.
results must exposes a method forecast(steps, **kwargs) that produces out-of-sample forecasts.
results may also exposes a method get_prediction that produces both in- and out-of-sample predictions.
>>> import numpy as np
>>> import pandas as pd
>>> from statsmodels.tsa.api import STLForecast
>>> from statsmodels.tsa.arima.model import ARIMA
>>> from statsmodels.datasets import macrodata
>>> ds = macrodata.load_pandas()
>>> data = np.log(ds.data.m1)
>>> base_date = f"{int(ds.data.year[0])}-{3*int(ds.data.quarter[0])+1}-1"
>>> data.index = pd.date_range(base_date, periods=data.shape[0], freq="QS")
>>> stlf = STLForecast(data, ARIMA, model_kwargs={"order": (2, 1, 0)})
>>> res = stlf.fit()
>>> forecasts = res.forecast(12)
Generate forecasts from an Exponential Smoothing model with trend >>> from statsmodels.tsa.statespace import exponential_smoothing >>> ES = exponential_smoothing.ExponentialSmoothing >>> config = {“trend”: True} >>> stlf = STLForecast(data, ES, model_kwargs=config) >>> res = stlf.fit() >>> forecasts = res.forecast(12)
fit(*[, inner_iter, outer_iter, fit_kwargs])
Estimate STL and forecasting model parameters.
fit(*[, inner_iter, outer_iter, fit_kwargs])
Estimate STL and forecasting model parameters.
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© Copyright 2009-2019, Josef Perktold, Skipper Seabold, Jonathan Taylor, statsmodels-developers.
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