Improving artificial neural networks' performance in seasonal time series forecasting

Information Sciences

Volumn 178, Issue 23, 2008, Pages 4550-4559

  Hamzaçebi, Coşkun ^a  

^a ZONGULDAK KARAELMAS UNIVERSITY   (Turkey)

Author keywords

Artificial neural networks; Forecasting; Holt Winters; Seasonal Box Jenkins model; Seasonal time series

Indexed keywords

ARTIFICIAL INTELLIGENCE; BACKPROPAGATION; FORECASTING; MODEL STRUCTURES; TIME SERIES ANALYSIS; VEGETATION;

ARTIFICIAL NEURAL NETWORK; ARTIFICIAL NEURAL NETWORKS; HOLT-WINTERS; INPUT AND OUTPUT; NETWORK STRUCTURES; PREDICTION ERRORS; REAL-WORLD TIME SERIES; SEASONAL BOX-JENKINS MODEL; SEASONAL TIME SERIES; SEASONALITY; STATISTICAL MODELLING; TIME-SERIES;

NEURAL NETWORKS;

EID: 52949087146     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2008.07.024     Document Type: Article

References (42)

1. Adya M., and Collopy F. How effective are neural networks at forecasting and prediction? A review and evaluation. Journal of Forecasting 17 (1998) 481-495
2. Alon I., Qi M., and Sadowski R.J. Forecasting aggregate retail sales: a comparison of artificial neural networks and traditional methods. Journal of Retailing and Consumer Services 8 3 (2001) 147-156
3. Barbounis T.G., and Teocharis J.B. Locally recurrent neural networks for wind speed prediction using spatial correlation. Information Sciences 177 (2007) 5775-5797
4. Box G.E.P., Jenkins G.M., and Reinsel G.C. Time Series Analysis, Forecasting and Control (1994), Prentice-Hall, Englewood Cliffs
5. Box G.E.P., and Jenkins G.M. Time Series Analysis Forecasting and Control (1976), Holden-Day, San Francisco
6. Brown R.G. Smoothing Forecasting and Prediction of Discrete Time Series (1962), Prentice Hall, Englewood Cliffs
7. Cadenas E., and Rivera W. Wind speed forecasting in the South Coast of Oaxaca, Mexico. Renewable Energy 32 (2007) 2116-2128
8. Catalao J.P.S., Mariano S.J.P.S., Mendes V.M.F., and Ferreira L.A.F.M. Short-term electricity prices forecasting in a competitive market: a neural network approach. Electric Power Systems Research 77 (2007) 1297-1304
9. Cybenko G. Approximation by superposition of a sigmoidal function. Mathematical Control Signal Systems 2 (1989) 303-314
10. Çelik A.E., and Karatepe Y. Evaluating and forecasting banking crises through neural network models: an application for Turkish banking sector. Expert Systems with Applications 33 (2007) 809-815
11. Demuth H., and Beale M. Neural Network Toolbox For Use With MATLAB User's Guide Version 4 (2000), MathWorks, MA
12. Faraway J., and Chatfield C. Time series forecasting with neural networks: a comparative study using the airline data. Applied Statistics 47 (1998) 231-250
13. Franses P.H., and Draisma G. Recognizing changing seasonal patterns using artificial neural networks. Journal of Econometrics 81 (1997) 273-280
14. Funahashi K. On the approximate realization of continuous mappings by neural networks. Neural Networks 2 (1989) 183-192
15. Hamzaçebi C. Forecasting of Turkey's net electricity energy consumption on sectoral bases. Energy Policy 35 3 (2007) 2009-2016
16. Hamzaçebi C., Akay D., and Kutay F. Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting. Expert Systems with Applications (2008) 10.1016/j.eswa.2008.02.042
17. Hill T., O'Connor M., and Remus W. Neural networks models for time series forecasts. Management Sciences 42 7 (1996) 1082-1092
18. Hornik K., Stinchcombe M., and White H. Multilayer feed-forward networks are universal approximators. Neural Networks 2 (1989) 359-366
19. Kaastra I., and Boyd M. Designing a neural network for forecasting financial and econometric time series. Neurocomputing 10 (1996) 215-236
20. Kline D.M. Methods for multi-step time series forecasting with neural networks. In: Zhang G.P. (Ed). Neural Networks for Business Forecasting (2004), Information Science Publishing, Hershey, PA 226-250
21. Kolarik T., and Rodorfer G. Time series forecasting using neural networks. APL Quote Quad 25 1 (1994) 86-94
22. Makridakis S., Wheelwright S., and Hyndman R.J. Forecasting Methods and Applications (1998), John Wiley & Sons, NY
23. Montgomery D.C., Johnson L.A., and Gardiner J.S. Forecasting and Time Series Analysis (1990), Mc-Graw Hill, NY
24. Nam K., and Schaefer T. Forecasting international airline passenger traffic using neural networks. Logistic and Transportation 31 3 (1995) 239-251
25. Nelson M., Hill T., Remus W., and O'Connor M. Time series forecasting using neural networks: should the data be deseasonalized first?. Journal of Forecasting 18 (1999) 359-367
26. M. Nelson, T. Hill, W. Remus, M. O'Connor, Can neural networks applied to time series forecasting learn seasonal patterns: an empirical investigation, in: Proceedings of the 27th Annual Hawaii International Conference on System Sciences, vol. 3, 1994, pp. 649-655.
27. Pai P., and Hong W. An improved neural network model in forecasting arrivals. Annals of Tourism Research 32 4 (2005) 1138-1141
28. Palmer A., Montano J.J., and Sese A. Designing an artificial neural network for forecasting tourism time series. Tourism Management 27 (2006) 781-790
29. Sahoo G.B., and Ray C. Flow forecasting for a Hawaii stream using rating curves and neural networks. Journal of Hydrology 317 (2006) 63-80
30. Sharda R., and Patil R.B. Connectionist approach to time series prediction: an empirical test. Journal of Intelligent Manufacturing 3 (1992) 317-323
31. Singh P., and Deo M.C. Suitability of different neural networks in daily flow forecasting. Applied Soft Computing 7 (2007) 968-978
32. Sözen A., Gülseven Z., and Arcaklioǧlu E. Forecasting based on sectoral energy consumption of GHGS in Turkey and mitigation policies. Energy Policy 35 (2007) 6491-6505
33. Tang Z., and Fishwick P.A. Feedforward neural nets as models for time series forecasting. ORSA Journal on Computing 5 4 (1993) 374-385
34. Tang Z., Almeida C., and Fishwick P.A. Time series forecasting using neural networks vs Box-Jenkins methodology. Simulation 57 5 (1991) 303-310
35. Tseng F.M., Yu H.C., and Tzeng G.H. Combining neural network model with seasonal time series ARIMA model. Technological Forecasting and Social Change 69 (2002) 71-87
36. Wang J.H., and Leu J.Y. Stock market trend prediction using ARIMA-based neural networks. IEEE International Conference on Neural Networks 4 6 (1996) 2160-2165
37. Wang T., and Chien S. Forecasting innovation performance via neural networks - a case of Taiwanese manufacturing industry. Technovation 26 (2006) 635-643
38. Zhang G., and Qi M. Neural network forecasting for seasonal and trend time series. European Journal of Operational Research 160 (2005) 501-514
39. Zhang G.P. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50 (2003) 159-175
40. Zhang G., Patuwo B.E., and Hu M.Y. Forecasting with artificial neural networks: the state of the art. International Journal of Forecasting 14 (1998) 35-62
41. Zhang X. Time series analysis and prediction by neural networks. Optimization Methods and Software 4 (1994) 151-170
42. Zhang G.P. A neural network ensemble method with jittered training data for time series forecasting. Information Sciences 177 (2007) 5329-5346