Arima Model Optimal Selection for Time Series Forecasting

Vadim Romanuke

Journal & Issue Details

Maritime Technical Journal

Journal Details

Format: Journal
eISSN: 2720-4286
First Published: 30 Mar 2016
Publication timeframe: 1 time per year
Languages: English

A fast-and-flexible method of ARIMA model optimal selection is suggested for univariate time series forecasting. The method allows obtaining as-highly-accurate-as-possible forecasts automatically. It is based on effectively finding lags by the autocorrelation function of a detrended time series, where the best-fitting polynomial trend is subtracted from the time series. The forecasting quality criteria are the root-mean-square error (RMSE) and the maximum absolute error (MaxAE) allowing to register information about the average inaccuracy and worst outlier. Thus, the ARIMA model optimal selection is performed by simultaneously minimizing RMSE and Max-AE, whereupon the minimum defines the best model. Otherwise, if the minimum does not exist, a combination of minimal-RMSE and minimal-MaxAE ARIMA models is used.

Keywords

time series forecasting
ARIMA
model selection
forecasting horizon
seasonality
trend

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