Space-time model versus VAR model: forecasting electricity demand in Japan. (English) Zbl 1397.60076
Summary: This paper examined the forecasting performance of disaggregated data with spatial dependency and applied it to forecasting electricity demand in Japan. We compared the performance of the spatial autoregressive ARMA (SAR-ARMA) model with that of the vector autoregressive (VAR) model from a Bayesian perspective. With regard to the log marginal likelihood and log predictive density, the VAR(1) model performed better than the SAR-ARMA( 1,1) model. In the case of electricity demand in Japan, we can conclude that the VAR model with contemporaneous aggregation had better forecasting performance than the SAR-ARMA model.
MSC:
| 60G25 | Prediction theory (aspects of stochastic processes) |
| 91G60 | Numerical methods (including Monte Carlo methods) |
Keywords:
spatial autoregressive model; vector autoregressive model; predictive function; disaggregated data; forecasting performanceReferences:
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