Forecasting strong seasonal time series with artificial neural networks

Journal of Scientific and Industrial Research

Volumn 71, Issue 10, 2012, Pages 657-666

  Adhikari, Ratnadip ^a   Agrawal, R K ^a  

^a JAWAHARLAL NEHRU UNIVERSITY   (India)

Author keywords

Artificial neural networks; Box jenkins model; Holt winters; Seasonal time series; Support vector machine; Time series forecasting

Indexed keywords

ALGORITHM; ARTIFICIAL NEURAL NETWORK; FORECASTING METHOD; GUIDELINE; NUMERICAL MODEL; PERFORMANCE ASSESSMENT; SEASONAL VARIATION; SEASONALITY; TIME SERIES;

EID: 84867668749     PISSN: 00224456     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (35)

1. Hipel K W & McLeod A I, Time series modeling of water resources and environmental systems (Elsevier Science Publishing Company, Amsterdam) 1994.
2. Box G E P & Jenkins G M, Time series analysis: Forecasting and control, 3rd edn (Holden-Day, California) 1970.
3. Chatfield C & Yar M, Holt-winters forecasting: some practical issues, J The Statistician 37 (1988) 129-140.
4. Vapnik V, The Nature of Statistical Learning Theory (Springer- Verlag, New York) 1995.
5. Fan Y, Li P & Song Z, Dynamic least square support vector machine, Proc IEEE 6th World Congress on Intelligent Control and Automation (Institute of Electrical and Electronics, Dalian) 2006, 4886-4889.
6. Cortez P, Sensitivity analysis for time lag selection to forecast seasonal time series using neural networks and support vector machines, in Int Joint Conf Neural Netw (Barcelona, Spain) 18-23 July 2010, 1-8.
7. Miller D M & Williams D, Damping seasonal factors: Shrinkage estimators for the X-12-ARIMA program, J Forecasting 20 (2004) 529-549.
8. Ghysels E, Granger C W & Siklos P L, Is seasonal adjustment a linear or nonlinear data filtering process, J Bus Econ Statist 14 (1996) 374-386.
9. Zhang, G P, Time series forecasting using a hybrid ARIMA and neural network model, J Neurocomputing 50 (2003) 159-175.
10. Zhang G, Patuwo B E & Hu M Y, Forecasting with artificial neural networks: The state of the art, J Forecasting 14 (1998) 35-62.
11. Kamruzzaman J, Begg R & Sarker R, Artificial Neural Networks in Finance and Manufacturing (Idea Group Publishing) 2006.
12. Hill T, O'Connor M & Remus W, Neural networks models for time series forecasts, J Manage Sci 42 (1996) 1082-1092
13. Zhang G & Qi M, Neural network forecasting for seasonal and trend time series, Eur J Oper Res 160 (2005) 501-514.
14. Alon I, Qi M & Sadowski R J, Forecasting aggregate retail sales: a comparison of artificial neural networks and traditional methods, J Retailing and Consumer Services8 (2001) 147-156.
15. Tseng F M, Yu H C & Tzeng G H, Combining neural network model with seasonal time series ARIMA model, J Technol Forecast Soc Change 69 (2002) 71-87.
16. Chatfield C, The Analysis of Time Series: An Introduction (Chapman Hall, Washington D C) 1996.
17. Cao L & Tay E H, Support vector machine with adaptive parameters in financial time series forecasting, J IEEE Trans Neural Netw 14 (2003) 1506-1508.
18. Hamzaçebi C, Improving artificial neural networks' performance in seasonal time series forecasting, J Inform Sci 178 (2006) 4 550-4559.
19. Kihoro J M, Otieno R O & Wafula C, Seasonal time series forecasting: A comparative study of ARIMA and ANN models, African J Sci and Technol 5 (2004) 41-49.
20. Faraway J & Chatfield C, Time series forecasting with neural networks: A comparative study using the airline data, J Appl Statist 47 (1998) 231-250.
21. Rumelhart D E, Hinton G E & Williams R J, Learning representations by back-propagating errors, J Nature 323 (1986) 533-536.
22. Hagan M & Menhaj M, Training feedforward networks with the marquardt algorithm, J IEEE Trans Neural Netw 5 (1994) 989-993.
23. Reidmiller M & Braun H, A direct adaptive method for faster backpropagation learning: The rprop algorithm, in IEEE Int Conf Neural Netw (San Francisco, USA) 1993, 586-591.
24. Moller M F, A scaled conjugate gradient algorithm for fast supervised learning, J Neural Networks 6 (1993) 525-533.
25. Battiti R, One step secant conjugate gradient, J Neural Comput 4 (1992) 141-166.
26. Dennis J E & Schnabel R B, Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Prentice-Hall Publishing, Englewood Cliffs, NJ) 1983.
27. Kennedy J, Eberhart R C & Shi Y, Swarm Intelligence (Morgan Kaufmann, San Francisco) 2001.
28. Adhikari R & Agrawal R K, Effectiveness of PSO Based Neural Network for Seasonal Time Series Forecasting, in Indian Int Conf on Artificial Intel (IICAI) (Siddaganga Institute of Technology Tumkur) 14-16 December 2006, 232-244.
29. Trelea I, The particle swarm optimization algorithm: convergence analysis and parameter selection, J Inf Process Lett 85 (2003) 317-325.
30. Hyndman R J, Time Series Data Library (TSDL), http://robjhyndman.com/TSDL/, 2010.
31. R Development Core Team, R: A Language and Environment for Statistical Computing, http://www.R-project.org (R Foundation for Statistical Computing, Vienna, Austria) 2011.
32. Demuth H, Beale M & Hagan M, Neural Network Toolbox User's Guide (The MathWorks, Natic, MA) 2010.
33. Birge B, PSOt-A Particle Swarm Optimization Toolbox for use with Matlab, in IEEE Swarm Intel Symp (Indianapolis, Indiana, USA) 24-26 April 2006.
34. Friedman M, The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Amer Statist Assoc 32 (1937) 675-701.
35. Hollander M & Wolfe D A, Nonparametric Statistical Methods (John Wiley & Sons, Inc, Hoboken, NJ) 1999.