Toward Automatic Time-Series Forecasting Using Neural Networks

Weizhong Yan

182 citations0/0 files storedIEEE Transactions on Neural Networks and Learning Systems

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Forecasting Techniques and Applications Neural Networks and Applications Time Series Analysis and Forecasting

Abstract

Over the past few decades, application of artificial neural networks (ANN) to time-series forecasting (TSF) has been growing rapidly due to several unique features of ANN models. However, to date, a consistent ANN performance over different studies has not been achieved. Many factors contribute to the inconsistency in the performance of neural network models. One such factor is that ANN modeling involves determining a large number of design parameters, and the current design practice is essentially heuristic and ad hoc, this does not exploit the full potential of neural networks. Systematic ANN modeling processes and strategies for TSF are, therefore, greatly needed. Motivated by this need, this paper attempts to develop an automatic ANN modeling scheme. It is based on the generalized regression neural network (GRNN), a special type of neural network. By taking advantage of several GRNN properties (i.e., a single design parameter and fast learning) and by incorporating several design strategies (e.g., fusing multiple GRNNs), we have been able to make the proposed modeling scheme to be effective for modeling large-scale business time series. The initial model was entered into the NN3 time-series competition. It was awarded the best prediction on the reduced dataset among approximately 60 different models submitted by scholars worldwide.

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2006Another look at measures of forecast accuracy 1998Forecasting with artificial neural networks:1989Combining forecasts: A review and annotated bibliography 2000The M3-Competition: results, conclusions and implications 1990Combining forecasts: A review and annotated bibliography 2001Principles of Forecasting 2000Out-of-sample tests of forecasting accuracy: an analysis and review 2010An Empirical Comparison of Machine Learning Models for Time Series Forecasting

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