This study presents an experimental evaluation of neural networks for nonlinear time-series forecasting. The e!ects of three main factors * input nodes, hidden nodes and sample size, are examined through a simulated computer experiment. Results show that neural networks are valuable tools for modeling and forecasting nonlinear time series while traditional linear methods are not as competent for this task. The number of input nodes is much more important than the number of hidden nodes in

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... network model building for forecasting. Moreover, large sample is helpful to ease the over"tting problem. Scope and purpose Interest in using arti"cial neural networks for forecasting has led to a tremendous surge in research activities in the past decade. Yet, mixed results are often reported in the literature and the e!ect of key modeling factors on performance has not been thoroughly examined. The lack of systematic approaches to neural network model building is probably the primary cause of inconsistencies in reported "ndings. In this paper, we present a systematic investigation of the application of neural networks for nonlinear time-series analysis and forecasting. The purpose is to have a detailed examination of the e!ects of certain important neural network modeling factors on nonlinear time-series modeling and forecasting.