1. bookVolume 224 (2022): Issue 1 (March 2022)
Journal Details
License
Format
Journal
eISSN
2720-4286
First Published
30 Mar 2016
Publication timeframe
1 time per year
Languages
English
access type Open Access

Arima Model Optimal Selection for Time Series Forecasting

Published Online: 08 Mar 2022
Volume & Issue: Volume 224 (2022) - Issue 1 (March 2022)
Page range: 28 - 40
Journal Details
License
Format
Journal
eISSN
2720-4286
First Published
30 Mar 2016
Publication timeframe
1 time per year
Languages
English
Abstract

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

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