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Neural network forecasting for seasonal and trend time series. (English) Zbl 1066.62094
Summary: 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.
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, in 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.

MSC:
62M45 Neural nets and related approaches to inference from stochastic processes
62M20 Inference from stochastic processes and prediction
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
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[1] Bell, W.R.; Hillmer, S.C., Issues involved with the seasonal adjustment of economic time series, Journal of business and economic statistics, 2, 291-320, (1984)
[2] Box, G.E.P.; Jenkins, G.M., Time series analysis: forecasting, and control, (1976), Holden Day San Francisco, CA · Zbl 0109.37303
[3] Cybenko, G., Approximation by superpositions of a Sigmoid function, Mathematics of control signals and systems, 2, 303-314, (1989) · Zbl 0679.94019
[4] Farway, J.; Chatfield, C., Time series forecasting with neural networks: A comparative study using the airline data, Applied statistics, 47, 231-250, (1995)
[5] Fildes, R.; Makridakis, S., The impact of empirical accuracy studies on time series analysis and forecasting, International statistical review, 63, 3, 289-308, (1995) · Zbl 0960.62535
[6] Findley, D.F.; Monsell, B.C.; Bell, W.R.; Otto, M.C.; Chen, B.C., New capabilities and methods of the X-12-ARIMA seasonal-adjustment program, Journal of business and economic statistics, 16, 2, 127-152, (1996)
[7] Franses, P.H., Recent advances in modelling seasonality, Journal of economic survey, 10, 3, 299-345, (1996)
[8] Franses, P.H.; Draisma, G., Recognizing changing seasonal patterns using artificial neural networks, Journal of econometrics, 81, 273-280, (1997) · Zbl 0904.62133
[9] Gardner, E.; McKenzie, E., Seasonal exponential smoothing with damped trends, Management science, 35, 3, 372-376, (1989) · Zbl 0709.62618
[10] Ghysels, E.; Granger, C.W.J.; Siklos, P.L., Is seasonal adjustment a linear or nonlinear data filtering process?, Journal of business and economics statistics, 14, 374-386, (1996)
[11] Goodrich, R.L., The forecast pro methodology, International journal of forecasting, 16, 533-535, (2000)
[12] Gorr, W.L., Research prospective on neural network forecasting, International journal of forecasting, 10, 1-4, (1994)
[13] Hansen, J.V.; Nelson, R.D., Forecasting and recombining time-series components by using neural networks, Journal of the operational research society, 54, 3, 307-317, (2003) · Zbl 1171.91369
[14] Hornik, K.; Stinchcombe, M.; White, H., Multilayer feedforward networks are universal approximators, Neural networks, 2, 359-366, (1989) · Zbl 1383.92015
[15] Hornik, K.; Stinchcombe, M.; White, H., Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks, Neural networks, 3, 551-560, (1990)
[16] Hylleberg, S., Seasonality in regression, (1986), Academic Press Orlando, FA · Zbl 0718.90021
[17] Hylleberg, S., General introduction, (), 3-14
[18] Hylleberg, S., Modelling seasonal variation, (), 153-178
[19] Ittig, P.T., A seasonal index for business, Decision sciences, 28, 2, 335-355, (1997)
[20] Kang, S., 1991. An investigation of the use of feedforward neural networks for forecasting. Ph.D. Dissertation, Kent State University, Kent, OH
[21] Kolarik, T.; Rudorfer, G., Time series forecasting using neural networks, APL quote quad, 25, 86-94, (1994)
[22] Makridakis, S.; Wheelwright, S.C., The handbook of forecasting: A Manager’s guide, (1987), Wiley New York
[23] Makridakis, S.; Anderson, A.; Carbone, R.; Fildes, R.; Hibdon, M.; Lewandowski, R.; Newton, J.; Parzen, E.; Winkler, R., The accuracy of extrapolation (time series) methods: results of a forecasting competition, Journal of forecasting, 1, 111-153, (1982)
[24] Nam, K.; Schaefer, T., Forecasting international airline passenger traffic using neural networks, Logistics and transportation review, 31, 3, 239-251, (1995)
[25] Nelson, C.R.; Plosser, C.I., Trends and random walks in macroeconomic time series: some evidence and implications, Journal of monetary economics, 10, 139-162, (1982)
[26] Nelson, M.; Hill, T.; Remus, T.; O’Connor, M., Time series forecasting using NNs: should the data be deseasonalized first?, Journal of forecasting, 18, 359-367, (1999)
[27] Pankratz, A., Forecasting with univariate box – jenkins models: concepts and cases, (1983), John Wiley & Sons New York
[28] Persons, W.M., Indices of business conditions, Review of economics and statistics, 1, 5-107, (1919)
[29] Persons, W.M., Correlation of time series, Journal of the American statistical association, 18, 713-726, (1923)
[30] Pierce, D.A., Relationships–and the lack of thereof–between economic time series, with special reference to money and interest rates, Journal of the American statistical association, 72, 11-26, (1977)
[31] Sharda, R.; Patil, R.B., Connectionist approach to time series prediction: an empirical test, Journal of intelligent manufacturing, 3, 317-323, (1992)
[32] Shiskin, J., Young, A.H., Musgrave, J.C., 1967. The X-11 variant of the census method II seasonal adjustment program. Technical Paper No. 15, US Department of Commerce, Bureau of Economic Analysis
[33] Tang, Z.; Fishwick, P.A., Feedforward neural nets as models for time series forecasting, ORSA journal of computing, 5, 4, 374-385, (1993) · Zbl 0789.62073
[34] Williams, P.M., Modelling seasonality and trends in daily rainfall data, (), 985-991
[35] Zhang, G., 1998. Linear and nonlinear time series forecasting with artificial neural networks. Ph.D. Dissertation, Kent State University, Kent, OH
[36] Zhang, G.; Patuwo, B.E.; Hu, M.Y., Forecasting with artificial neural networks: the state of the art, International journal of forecasting, 14, 35-62, (1998)
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