Advances in automatic time series forecasting

Host Institution:

La Trobe University

Title of Seminar:

Advances in automatic time series forecasting

Speaker's Name:

Professor Rob J Hyndman

Speaker's Institution:

Monash University

Time and Date:

Friday 19 April, 12.30pm (AEST)

Seminar 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 describe some algorithms for automatically forecasting various types of time series, including:

  • univariate non-seasonal time series;
  • univariate seasonal time series;
  • univariate time series with multiple seasonality;
  • functional time series;
  • hierarchical time series.

In addition to providing automatic forecasts when required, these algorithms also provide high quality benchmarks that can be used when developing more specific and specialized forecasting models. All the methods described are freely available in R packages.

Seminar Convenor:

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