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Title: TIME SERIES FORECASTING USING HIGH ORDER ARIMA FUNCTIONS
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TIME SERIES FORECASTING USING HIGH ORDER ARIMA FUNCTIONS
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1314-2704
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English
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19
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2.1
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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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conference
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19th International Multidisciplinary Scientific GeoConference SGEM 2019
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19th International Multidisciplinary Scientific GeoConference SGEM 2019, 30 June - 6 July, 2019
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Proceedings Paper
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STEF92 Technology
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International Multidisciplinary Scientific GeoConference-SGEM
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Bulgarian Acad Sci; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci & Arts; Slovak Acad Sci; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; World Acad Sci; European Acad Sci, Arts & Letters; Ac
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673-680
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30 June - 6 July, 2019
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website
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cdrom
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5410
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ARIMA; time series; forecast; Akaike information criterion (AIC); Bayes information criterion (BIC).
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