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TIME SERIES FORECASTING USING HIGH ORDER ARIMA FUNCTIONS

D. A. Petrusevich

First published: 2019-06-20https://doi.org/10.5593/sgem2019/2.1/s07.088View metrics

Abstract

The changes of the welfare of the Russian citizens are explored in the paper. The time diapason taken into account is: 2000 ? 2018. The monthly wage index based on the Dynamic series of macroeconomic statistics of the Russian Federation data (2000-2018) has been explored. The mathematical models of the wage index of this time period have been presented. They are based on the ARIMA (p, d, q) models with p, q less than or equal to 5 (Autoregressive integrated moving average). Forecasts of these models are compared to predictions of the models with parameters p = 6 or q = 6. The constructed models have made better forecasts than the automatically fitted ARIMA ones with p, d less than 6. They have been compared using two metrics and also the Akaike and Bayes information criterion (AIC, BIC) has been considered. The seasonal factors of the wage index have been taken into account. It has been shown that the lags of 6 and 12 months are connected to the today wage index; maxima of this value are situated at the end of the year and in summer. It?s explained with the vacations which traditionally take place in summer and officially held vacations in January

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Publication details

Title
TIME SERIES FORECASTING USING HIGH ORDER ARIMA FUNCTIONS
Authors
D. A. Petrusevich
Proceedings
SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings; 19th International Multidisciplinary Scientific GeoConference SGEM2019, Informatics, Geoinformatics and Remote Sensing
Publisher
STEF92 Technology
Year
2019
Pages
673-680
SWS Citekey
Petrusevich20197673680
ISSN
1314-2704
ISBN
978-619-7408-79-9
Language
en
Publication type
Conference Paper
Keywords
ARIMAtime seriesforecastAkaike information criterion (AIC)Bayes information criterion (BIC).
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