<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

Arima Model Optimal Selection for Time Series Forecasting

Maritime Technical Journal

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Arima Model Optimal Selection for Time Series Forecasting"}

A fast-and-flexible method of ARIMA model optimal selection is suggested for univariate time series forecasting. The method allows obtaining...

Arima Model Optimal Selection for Time Series Forecasting

Online veröffentlicht: 08. März 2022

Seitenbereich: 28 - 40

DOI: https://doi.org/10.2478/sjpna-2022-0003

Schlüsselwörter
time series forecasting, ARIMA, model selection, forecasting horizon, seasonality, trend

© 2020 Vadim Romanuke, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Arima Model Optimal Selection for Time Series Forecasting

Online veröffentlicht: 08. März 2022

Seitenbereich: 28 - 40

DOI: https://doi.org/10.2478/sjpna-2022-0003

Schlüsselwörtertime series forecasting, ARIMA, model selection, forecasting horizon, seasonality, trend

© 2020 Vadim Romanuke, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
time series forecasting, ARIMA, model selection, forecasting horizon, seasonality, trend