3 Statsmodels Tricks for Time Series Analysis & Forecasting
The article presents three efficient techniques for using the statsmodels library to perform time series analysis and forecasting. It highlights methods for retrieving prediction intervals, updating models with new data without refitting, and handling seasonality using STLForecast. These approaches leverage built-in object methods to improve accuracy and reduce manual coding errors.
- ▪Using get_forecast instead of forecast provides access to uncertainty intervals like confidence bounds that are already computed by the model.
- ▪The append method allows new observations to be added to a fitted model without re-estimating parameters, which is faster than refitting the entire dataset.
- ▪STLForecast automates the process of decomposing seasonality, forecasting the deseasonalized data, and re-adding the seasonal component to the final prediction.
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A fitted statsmodels model computes a more than just the array of numbers most code pulls out of it. The point forecast is the smallest task it can perform. Every trick runs from a case of asking the results object for something it has already worked out, rather than having to rebuild that thing by hand. One dataset, one model, three methods people routinely reimplement. All three are run against the same monthly series and the same fitted model, so the only thing that changes between them is which method gets called on the object fit handed back. Everything below was checked against statsmodels 0.15.0. Start by installing statsmodels: pip install statsmodels Trick 1: Asking for the Interval, Not Just the Number res.forecast(12) gives you twelve numbers.
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