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Dynamic Bayesian smooth transition autoregressive models applied to hourly electricity load in Brazil

Faria, Álvaro E. and Santos, Alexandre J. (2019). Dynamic Bayesian smooth transition autoregressive models applied to hourly electricity load in Brazil. In: Valenzuela, Olga; Rojas, Fernando; Pomares, Hector and Rojas, Ignacio eds. Theory and Applications of Time Series Analysis. Contributions to Statistics. Springer, (In Press).

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Dynamic Bayesian Smooth Transition Autoregressive (DBSTAR) models are proposed for nonlinear autoregressive time series processes as alternative to both the classical Smooth Transition Autoregressive (STAR) models of Chan and Tong (1986) and the Bayesian Simulation STAR (BSTAR) models of Lopes and Salazar (2005). Unlike those, DBSTAR models are sequential polynomial dynamic analytical models suitable for inherently non-stationary time series with non-linear characteristics such as asymmetric cycles. As they are analytical, they also avoid potential computational problems associated with BSTAR models and allow fast sequential estimation of parameters.

Two types of DBSTAR models are defined here based on the method adopted to approximate the transition function of their autoregressive components, namely the Taylor and the B-splines DBSTAR models. A harmonic version of those models, that accounted for the cyclical component explicitly in a flexible yet parsimonious way, were applied to the well-known series of annual Canadian lynx trappings and showed improved fitting when compared to both the classical STAR and the BSTAR models. Another application to a long series of hourly electricity loading in southern Brazil, covering the period of the South-African Football World Cup in June 2010, illustrates the short-term forecasting accuracy of fast computing harmonic DBSTAR models that account for various characteristics such as periodic behaviour (both within-the-day and within-the-week) and average temperature.

Item Type: Book Section
Keywords: Bayesian dynamic STAR models; polynomial forecasting models; nonlinear autoregressive models; Bayesian autoregressive forecasting models; short-term electricity load forecasting; B-splines approximation
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 59668
Depositing User: Álvaro Faria
Date Deposited: 19 Mar 2019 11:55
Last Modified: 12 Sep 2019 23:31
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