Forecasting and recombining time-series components by using neural networks

Operations and other business decisions often depend on accurate time-series forecasts. These time series usually consist of trend-cycle, seasonal, and irregular components. Existing methodologies attempt to first identify and then extrapolate these components to produce forecasts. The proposed process partners this decomposition procedure with neural network methodologies to combine the strengths of economics, statistics, and machine learning research. Stacked generalization first uses transformations and decomposition to pre-process a time series. Then a time-delay neural network receives the resulting components as inputs. The outputs of this neural network are then input to a backpropagation algorithm that synthesizes the processed components into a single forecast. Genetic algorithms guide the architecture selection for both the time-delay and backpropogation neural networks. The empirical examples used in this study reveal that the combination of transformation, feature extraction, and neural networks through stacked generalization gives more accurate forecasts than classical decomposition or ARIMA models. The research reported in this paper examines two concurrent issues. The first evaluates the performance of neural networks in forecasting time series. The second assesses the use of stacked generalization as a way of refining this process. The methodology is applied to four economic and business time series. Those studying time series and neural networks, particularly in terms of combining tools from the statistical community with neural network technology, will find this paper relevant.