|
Peter Zhang
J. Mack Robinson
Associate Professor
Profile:
Ph.D., Kent State University
Specializations:
Forecasting
Neural Networks
Inventory Management
Quality Control
More info:
Bio info>>
|
|
Computing, Artificial Intelligence and Information Technology
Neural network forecasting for seasonal and trend time series
G. Peter Zhang, J. Mack Robinson College of Business, Georgia State University and Min Qi, College of Business Administration, Kent State University
Journal: European Journal of Operational Research (16) 2005
Abstract: Neural networks have been widely used as a promising method for time series forecasting. However, limited empirical studies on seasonal time series forecasting with neural networks yield mixed results. While some find that neural networks are able to model seasonality directly and prior deseasonalization is not necessary, others conclude just the opposite. In this paper, we investigate the issue of how to effectively model time series with both seasonal and trend patterns. In particular, we study the effectiveness of data preprocessing, including deseasonalization and detrending, on neural network modeling and forecasting performance. Both simulation and real data are examined and results are compared to those obtained from the Box–Jenkins seasonal autoregressive integrated moving average models. We find that neural networks are not able to capture seasonal or trend variations effectively with the unpreprocessed raw data and either detrending or deseasonalization can dramatically reduce forecasting errors. Moreover, a combined detrending and deseasonalization is found to be the most effective data preprocessing approach.
Keywords: Neural networks; Box–Jenkins method; Seasonality; Time series; Forecasting |
