Chapter

Principles of Forecasting

Volume 30 of the series International Series in Operations Research & Management Science pp 245-256

Neural Networks for Time-Series Forecasting

  • William RemusAffiliated withDepartment of Decision Science, University of Hawaii
  • , Marcus O’ConnorAffiliated withSchool of Information Systems, University of New South Wales

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Abstract

Neural networks perform best when used for (1) monthly and quarterly time series, (2) discontinuous series, and (3) forecasts that are several periods out on the forecast horizon. Neural networks require the same good practices associated with developing traditional forecasting models, plus they introduce new complexities. We recommend cleaning data (including handling outliers), scaling and deseasonalizing the data, building plausible neural network models, pruning the neural networks, avoiding overfitting, and good implementation strategies.

Keywords

Discontinuities forecasting neural networks principles seasonality