Advances in automatic time series forecasting

Published on 19 June 2012 in Talks

Abstract: Many applications require a large number of time series to be forecast completely automatically. For example, manufacturing companies often require weekly forecasts of demand for thousands of products at dozens of locations in order to plan distribution and maintain suitable inventory stocks. In population forecasting, there are often a few hundred time series to be forecast, representing various components that make up the population dynamics. In these circumstances, it is not feasible for time series models to be developed for each series by an experienced statistician. Instead, an automatic forecasting algorithm is required.

I will look at some algorithms recently developed for automatically forecasting various types of time series, including approaches for handling functional time series, hierarchical time series, and time series with multiple seasonality.

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