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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 auto-matically. 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 fore-casting quality criteria...
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