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Dynamic Bayesian models for non-linear auto-regressive processes

This research investigates alternative Bayesian approaches to the existing static smooth transition auto-regressive (STAR) models for auto-regressive non-linear time series. The approaches we propose are dynamic and analytic being thus suitable for non-stationary real-time AR processes such as those exhibiting asymmetric cycles and/or sudden changes of level and variability. Examples include unemployment rates, industrial production and hourly electricity consumption and prices data.

An application to a time series of hourly electricity consumption (in Mega Watts) in southern Brazil, during the South-Africa Football World Cup in June 2010, showed the forecasts produced by the dynamic model (24-hours in advance) to quickly adapt to calendar effects and to be quite accurate. The figure below shows the forecasts, the 95% prediction intervals and the observed hourly electricity consumptions for the 13-19 June 2010 week. Note the sudden drop in consumption on Tuesday, 15 June 2010, afternoon when Brazil played its first match against North Korea.

Graph showing close alignment between a forcast and observed electricty consumption over a week


More details about dynamic Bayesian STAR models can be found here.