statsmodels.tsa.forecasting.stl.STLForecast¶

class
statsmodels.tsa.forecasting.stl.
STLForecast
(endog, model, *, model_kwargs=None, 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)[source]¶ Modelbased forecasting using STL to remove seasonality
Forecasts are produced by first subtracting the seasonality estimated using STL, then forecasting the deseasonalized data using a timeseries model, for example, ARIMA.
 Parameters
 endogarray_like
Data to be decomposed. Must be squeezable to 1d.
 model
Model
The model used to forecast endog after the seasonality has been removed using STL
 model_kwargs
Dict
[str
,Any
] Any additional arguments needed to initialized the model using the residuals produced by subtracting the seasonality.
 period{
int
,None
},optional
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.
 seasonal
int
,optional
Length of the seasonal smoother. Must be an odd integer, and should normally be >= 7 (default).
 trend{
int
,None
},optional
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.
 low_pass{
int
,None
},optional
Length of the lowpass filter. Must be an odd integer >=3. If not provided, uses the smallest odd integer > period.
 seasonal_deg
int
,optional
Degree of seasonal LOESS. 0 (constant) or 1 (constant and trend).
 trend_deg
int
,optional
Degree of trend LOESS. 0 (constant) or 1 (constant and trend).
 low_pass_deg
int
,optional
Degree of low pass LOESS. 0 (constant) or 1 (constant and trend).
 robustbool,
optional
Flag indicating whether to use a weighted version that is robust to some forms of outliers.
 seasonal_jump
int
,optional
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.
 trend_jump
int
,optional
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.
 low_pass_jump
int
,optional
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.
See also
statsmodels.tsa.arima.model.ARIMA
ARIMA modeling.
statsmodels.tsa.ar_model.AutoReg
Autoregressive modeling supporting complex deterministics.
statsmodels.tsa.exponential_smoothing.ets.ETSModel
Additive and multiplicative exponential smoothing with trend.
statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing
Additive exponential smoothing with trend.
Notes
If \(\hat{S}_t\) is the seasonal component, then the deseasonalize series is constructed as
\[Y_t  \hat{S}_t\]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 outofsample forecasts of the seasonal component are produced as
\[\hat{S}_{T + h} = \hat{S}_{T  k}\]where \(k = m  h + m \lfloor (h1)/m \rfloor\) 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 userspecified 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 methodforecast(steps, **kwargs)
that produces outofsample forecasts.results
may also exposes a methodget_prediction
that produces both in and outofsample predictions.
Examples
>>> 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")
Generate forecasts from an ARIMA
>>> 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)
Methods
fit
(*[, inner_iter, outer_iter, fit_kwargs])Estimate STL and forecasting model parameters.
Methods
fit
(*[, inner_iter, outer_iter, fit_kwargs])Estimate STL and forecasting model parameters.