A neural network approach to forecasting model selection

Abstract The literature has shown that no one model provides the most accurate forecasts. The focus has instead shifted to identifying the characteristics of the time series in order to provide guidelines for choosing the most appropriate extrapolation model. In this paper we test the feasibility of employing the neural network structure for model selection. To accomplish this objective, a set of time series characteristics, representing the domain knowledge, is established. A back propagation neural network is then constructed with eleven input nodes representing six time series characteristics. The output nodes of the neural network represent nine time series forecasting methods grouped into three categories. The results indicate that the neural network approach can assist the practitioner in the selection of the appropriate forecast model.

[1]  G Kohers.  The use of modular neural networks in time series forecasting. , 1992 .

[2]  Kishan G. Mehrotra,et al.  Forecasting the behavior of multivariate time series using neural networks , 1992, Neural Networks.

[3]  Vincent A. Mabert,et al.  Constrained Forecasting: Some Implementation Guidelines , 1992 .

[4]  Fred L. Collopy,et al.  Error Measures for Generalizing About Forecasting Methods: Empirical Comparisons , 1992 .

[5]  Jeffrey Sohl,et al.  Disaggregation methods to expedite product line forecasting , 1990 .

[6]  Fred Collopy,et al.  Expert Opinions About Extrapolation and the Mystery of the Overlooked Discontinuities , 1992 .

[7]  Robert A. Marose.  A financial neural-network application , 1990 .

[8]  David Bailey,et al.  How to develop neural-network applications , 1990 .

[9]  Marcus O'Connor,et al.  Time series characteristics and the widths of judgemental confidence intervals , 1992 .

[10]  Lyle H. Ungar,et al.  Neural network forecasting of short, noisy time series , 1992 .

[11]  J. Utans,et al.  Selecting neural network architectures via the prediction risk: application to corporate bond rating prediction , 1991, Proceedings First International Conference on Artificial Intelligence Applications on Wall Street.

[12]  C. P. Sheppard,et al.  Predicting time series by a fully connected neural network trained by back propagation , 1992 .

[13]  Peter J. Danaher,et al.  Predictive accuracy of simple versus complex econometric market share models: Theoretical and empirical results , 1992 .

[14]  Paul A. Fishwick,et al.  Time series forecasting using neural networks vs. Box- Jenkins methodology , 1991, Simul..

[15]  Douglas J. Dalrymple.  Sales forecasting practices: Results from a United States survey , 1987 .

[16]  Steven P. Schnaars,et al.  Situational Factors Affecting Forecast Accuracy , 1984 .

[17]  Soumitra Dutta,et al.  Bond rating: A non-conservative application of neural networks , 1988 .

[18]  Fred Collopy,et al.  Rule-Based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations , 1992 .

[19]  Desmond Fletcher,et al.  Forecasting with neural networks: An application using bankruptcy data , 1993, Inf. Manag..

[20]  David Bailey,et al.  Developing neural-network applications , 1990 .

[21]  Robert L. Winkler,et al.  The accuracy of extrapolation (time series) methods: Results of a forecasting competition , 1982 .

[22]  Andrew Zardecki,et al.  Rule-Based Forecasting , 1996 .

[23]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[24]  Robert Fildes,et al.  The evaluation of extrapolative forecasting methods , 1992 .

[25]  E. Mine Cinar,et al.  Neural Networks: A New Tool for Predicting Thrift Failures , 1992 .

[26]  A. S. Weigend,et al.  Results of the time series prediction competition at the Santa Fe Institute , 1993, IEEE International Conference on Neural Networks.