Automatic time series forecasting
نویسنده
چکیده
Automatic forecasts of large numbers of univariate time series are often needed in business. It is common to have over one thousand product lines that need forecasting at least monthly. In these circumstances, an automatic forecasting algorithm is an essential tool. Automatic forecasting algorithms must determine an appropriate time series model, estimate the parameters and compute the forecasts. The most popular automatic forecasting algorithms are based on either exponential smoothing or ARIMA models.
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