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Time Series Prediction With Genetic‐Algorithm Designed Neural Networks: An Empirical Comparison With Modern Statistical Models
Author(s) -
Hansen James V.,
McDonald James B.,
Nelson Ray D.
Publication year - 1999
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/0824-7935.00090
Subject(s) - autoregressive model , artificial neural network , computer science , series (stratigraphy) , time series , autoregressive integrated moving average , artificial intelligence , genetic algorithm , moving average , machine learning , ordinary least squares , algorithm , pattern recognition (psychology) , mathematics , econometrics , computer vision , biology , paleontology
Neural networks whose architecture is determined by genetic algorithms outperform autoregressive integrated moving average forecasting models in six different time series examples. Refinements to the autoregressive integrated moving average model improve forecasting performance over standard ordinary least squares estimation by 8% to 13%. In contrast, neural networks achieve dramatic improvements of 10% to 40%. Additionally, neural networks give evidence of detecting patterns in data which remain hidden to the autoregression and moving average models. The consequent forecasting potential of neural networks makes them a very promising addition to the variety of techniques and methodologies used to anticipate future movements in time series.