Forecasting Time Series with Multiple Seasonal Cycles Using Neural Networks with Local Learning

Abstract

AbstractIn the article a simple neural model with local learning for forecasting time series with multiple seasonal cycles is presented. This model uses patterns of the time series seasonal cycles: input ones representing cycles preceding the forecast moment and forecast ones representing the forecasted cycles. Patterns simplify the forecasting problem especially when a time series exhibits nonstationarity, heteroscedasticity, trend and many seasonal cycles. The artificial neural network learns using the training sample selected from the neighborhood of the query pattern. As a result the target function is approximated locally which leads to a reduction in problem complexity and enables the use of simpler models. The effectiveness of the proposed approach is illustrated through applications to electrical load forecasting and compared with ARIMA and exponential smoothing approaches. In a day ahead load forecasting simulations indicate the best results for the one-neuron network.Keywordsseasonal time series forecastingshort-term load forecastinglocal learningneural networks