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Forecasting with autocorrelations and partial autocorrelations

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Time series models provide an avenue for long-term forecasting. There are quite a few methods: Markov models; Holt-Winters method, Box-Jenkins Multiplicative Seasonal Autoregressive Integrated Moving Average (SARIMA).

The selection of an appropriate model for analyzing a particular problem depends on many factors, such as, number of series to be forecasted, required accuracy of forecasts, modeling costs, ease of use of the models, ease of interpretation of the forecasts, etc.

Seasonal and/or nonseasonal ...

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The solution is cited and thorough in expanding the topic of seasonal forecasting.