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ARIMA and SARIMA are both algorithms for forecasting. ARIMA takes into account the past values (autoregressive, moving average) and predicts future values based on that. SARIMA similarly uses past values but also takes into account any seasonality patterns. Since SARIMA brings in seasonality as a parameter, it’s significantly more powerful than ARIMA in forecasting complex data spaces containing cycles.

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Author of the review:
Spiros Sirmakessis
University of Peloponnese


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Eligius Hendrix

The idea of ARIMA is that we can study the autocorrelation function for all lags in a time series. This means that one can also detect seasonality if it exists, for instance in lag 12 if we are talking monthly data and lag 52 if we are talking weekly data. Just to take into account that the original statistical models already measure seasonal behaviour.