A Novel Layer Based Ensemble Architecture for Time Series Forecasting

Time series forecasting (TSF) have been widely used in many application areas such as science, engineering, and finance. Usually the characteristics of phenomenon generating a series are unknown and the information available for forecasting is limited to the past values of the series. It is, therefore, important to use an appropriate number of past values, termed lag, for forecasting. Although ensembles (combining several learning machines) have been widely used for classification problems, there is only a handful work for TSF problems. Existing algorithms for TSF construct ensembles by combining base predictors involving different training parameters or data sets . The idea of ensemble is also employed to find the optimal parameter of predictors used for TSF. The aim of using different parameters or data sets is to maintain diversity among the learning machines in an ensemble. It has been known that the performance of ensembles greatly depends not only on diversity but also on accuracy of the learning machines. However, the issue of accuracy is totally ignored in ensemble approaches used for forecasting. This thesis proposes a layered ensemble architecture (LEA) for TSF. Our LEA is consisted of two layers. Each of the layers uses a neural network ensemble. However, tasks of ensembles in the two layers are different. While the ensemble of the first layer tries to find an appropriate time window of a given time series, it of the second layer makes prediction using the time window obtained from the lower (first) layer. For maintaining diversity, LEA uses a different training set for each network in the ensemble of the first and second layers. LEA has been tested extensively on the time series data sets of NN3 competition. In terms of prediction accuracy, our experimental results have showed clearly that LEA is better than other ensemble and nonensemble algorithms.