Neural Networks for Time-Series Forecasting

  • William Remus
  • Marcus O’Connor
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 30)

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 

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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • William Remus
    • 1
  • Marcus O’Connor
    • 2
  1. 1.Department of Decision ScienceUniversity of HawaiiUSA
  2. 2.School of Information SystemsUniversity of New South WalesAustralia

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