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A hybrid linear-neural model for time series forecasting

2000; Institute of Electrical and Electronics Engineers; Volume: 11; Issue: 6 Linguagem: Inglês

10.1109/72.883463

ISSN

1941-0093

Autores

Álvaro Veiga, Marcelo C. Medeiros,

Tópico(s)

Energy Load and Power Forecasting

Resumo

This paper considers a linear model with time varying parameters controlled by a neural network to analyze and forecast nonlinear time series.We show that this formulation, called neural coefficient smooth transition autoregressive (NCSTAR) model, is in close relation to the threshold autoregressive (TAR) model and the smooth transition autoregressive (STAR) model with the advantage of naturally incorporating linear multivariate thresholds and smooth transitions between regimes. In our proposal, the neuralnetwork output is used to induce a partition of the input space, with smooth and multivariate thresholds. This also allows the choice of good initial values for the training algorithm.

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