Find out how to access previewonly content
Neural Networks for TimeSeries Forecasting
 G. Peter Zhang
 … show all 1 hide
Abstract
Neural networks has become an important method for time series forecasting. There is increasing interest in using neural networks to model and forecast time series. This chapter provides a review of some recent developments in time series forecasting with neural networks, a brief description of neural networks, their advantages over traditional forecasting models, and some recent applications. Several important data and modeling issues for time series forecasting are highlighted. In addition, recent developments in several methodological areas such as seasonal time series modeling, multiperiod forecasting, and the ensemble method are reviewed.
Page
%P
Inside
Within this Entry
 Introduction
 Neural Networks
 Applications in Time Series Forecasting
 Neural Network Modeling Issues
 Methodological Issues
 Conclusions
 References
 References
 AbdelAal RE (2008) Univariate modeling and forecasting of monthly energy demand time series using abductive and neural networks. Comput Ind Eng 54:903–917
 Aburto L, Weber R (2007) Improved supply chain management based on hybrid demand forecasts. Appl Soft Comput 7(1):136–144
 Adya M, Collopy F (1998) How effective are neural networks at forecasting and prediction? A review and evaluation. J Forecasting 17:481–495
 Ang A, Piazzesi M, Wei M (2006) What does the yield curve tell us about GDP growth? J Econometrics 131:359–403
 Armano G, Marchesi M, Murru A (2005) A hybrid geneticneural architecture for stock indexes forecasting. Info Sci 170(1):3–33
 Armstrong JS (2001) Principles of forecasting: A handbook for researchers and practitioners. Kluwer, Boston, MA
 Ashley R (2003) Statistically significant forecasting improvements: how much outofsample data is likely necessary? Int J Forecasting 19(2):229–239
 Aslanargun A, Mammadov M, Yazici B, Yolacan S (2007) Comparison of ARIMA, neural networks and hybrid models in time series: tourist arrival forecasting. J Stat Comput Simulation 77(1):29–53
 Atiya AF, ElShoura SM, Shaheen SI, ElSherif MS (1999) A comparison between neuralnetwork forecasting techniquescase study: river flow forecasting. IEEE Trans Neural Netw 10(2):402–409
 Azadeh A, Ghaderi SF, Sohrabkhani S (2007) Forecasting electrical consumption by integration of neural network, time series and ANOVA. Appl Math Comput 186:1753–1761
 Azoff EM (1994) Neural network time series forecasting of financial markets. Wiley, Chichester, UK
 Bakirtzis AG, Petridis V, Kiartzis SJ, Alexiadis MC, Maissis AH (1996) A neural network short term load forecasting model for the Greek power system. IEEE Trans Power Syst 11(2):858–863
 Balkin DS, Ord KJ (2000) Automatic neural network modeling for univariate time series. Int J Forecasting 16:509–515
 Barreto GA (2008) Time series prediction with the selforganizing map: a review. Stud Comput Intell 77:135–158
 Berardi LV, Zhang PG (2003) An empirical investigation of bias and variance in time series forecasting: modeling considerations and error evaluation. IEEE Trans Neural Netw 14(3):668–679
 Bhansali RJ (1997) Direct autoregressive predictions for multistep prediction: order selection and performance relative to the plug in predictors. Stat Sin 7:425–449
 Bishop M (1995) Neural networks for pattern recognition. Oxford University Press, Oxford
 Bodyanskiy Y, Popov S (2006) Neural network approach to forecasting of quasiperiodic financial time series. Eur J Oper Res 175:1357–1366
 Box GEP, Jenkins G (1976) Time series analysis: forecasting and control. HoldenDay, San Francisco, CA
 Cadenas E, Rivera W (2009) Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks. Renewable Energy 34(1):274–278
 Cai X, Zhang N, Venayagamoorthy GK, Wunsch DC (2007) Time series prediction with recurrent neural networks trained by a hybrid PSOEA algorithm. Neurocomputing 70:2342–2353
 Chakraborty K, Mehrotra K, Mohan KC, Ranka S (1992) Forecasting the behavior of multivariate time series using neural networks. Neural Netw 5:961–970
 Chatfield C (2001) Timeseries forecasting. Chapman & Hall/CRC, Boca Raton, FL
 Chelani AB, Devotta S (2007) Prediction of ambient carbon monoxide concentration using nonlinear time series analysis technique. Transportation Res Part D 12:596–600
 Chevillon G, Hendry DF (2005) Nonparametric direct multistep estimation for forecasting economic processes. Int J Forecasting 21:201–218
 Chu FL (2008) Analyzing and forecasting tourism demand with ARAR algorithm. Tourism Manag 29(6):1185–1196
 Coakley JR, Brown CE (1999) Artificial neural networks in accounting and finance: modeling issues. Int J Intell Syst Acc Finance Manag 9:119–144
 Coman A, Ionescu A, Candau Y (2008) Hourly ozone prediction for a 24h horizon using neural networks. Environ Model Software 23(12):1407–1421
 Connor JT, Martin RD, Atlas LE (1994) Recurrent neural networks and robust time series prediction. IEEE Trans Neural Netw 51(2):240–254
 Cottrell M, Girard B, Girard Y, Mangeas M, Muller C (1995) Neural modeling for time series: a statistical stepwise method for weight elimination. IEEE Trans Neural Netw 6(6):1355–1364
 De Gooijer JG, Kumar K (1992) Some recent developments in nonlinear time series modeling, testing, and forecasting. Int J Forecasting 8:135–156
 De Groot C, Wurtz D (1991) Analysis of univariate time series with connectionist nets: a case study of two classical examples. Neurocomputing 3:177–192
 Doganis P, Aggelogiannaki E, Patrinos P, Sarimveis H (2006) Time series sales forecasting for short shelflife food products based on artificial neural networks and evolutionary computing. J Food Eng 75:196–204
 Doganis P, Aggelogiannaki E, Sarimveis H (2008) A combined model predictive control and time series forecasting framework for productioninventory systems. Int J Prod Res 46(24):6841–6853
 Dougherty M (1995) A review of neural networks applied to transport. Transportation Res Part C 3(4):247–260
 Egrioglu E, Aladag CAH, Gunay S (2008) A new model selection strategy in artificial neural networks. Appl Math Comput 195:591–597
 Fadlalla A, Lin CH (2001) An analysis of the applications of neural networks in finance. Interfaces 31(4):112–122
 Fahlman S, Lebiere C (1990) The cascadecorrelation learning architecture. In: Touretzky D (ed) Advances in neural information processing systems, vol 2. Morgan Kaufmann, Los Altos, CA, pp 524–532
 Farway J, Chatfield C (1995) Time series forecasting with neural networks: a comparative study using the airline data. Appl Stat 47:231–250
 Findley DF (1985) Model selection for multistepahead forecasting. In: Baker HA, Young PC (eds) Proceedings of the seventh symposium on identification and system parameter estimation. Pergamon, Oxford, New York, pp 1039–1044
 Franses PH, Draisma G (1997) Recognizing changing seasonal patterns using artificial neural networks. J Econometrics 81:273–280
 Frean M (1990) The Upstart algorithm: A method for constructing and training feedforward networks. Neural Comput 2:198–209
 Freitas and Rodrigues (2006) Model combination in neuralbased forecasting. Eur J Oper Res 173:801–814
 Gately E (1996) Neural networks for financial forecasting. Wiley, New York
 Gautama AK, Chelanib AB, Jaina VK, Devotta S (2008) A new scheme to predict chaotic time series of air pollutant concentrations using artificial neural network and nearest neighbor searching. Atmospheric Environ 42:4409–4417
 Ghiassi M, Saidane H, Zimbra DK (2005) A dynamic artificial neural network model for forecasting time series events. Int J Forecasting 21(2):341–362
 Ghysels E, Granger CWJ, Siklos PL (1996) Is seasonal adjustment a linear or nonlinear data filtering process? J Bus Econ Stat 14:374–386
 Ginzburg I, Horn D (1994) Combined neural networks for time series analysis. Adv Neural Info Process Syst 6:224–231
 Goh YW, Lim PC, Peh KK (2003) Predicting drug dissolution profiles with an ensemble of boosted neural networks: A time series approach. IEEE Trans Neural Netw 14(2):459–463
 Gorr L (1994) Research prospective on neural network forecasting. Int J Forecasting 10:1–4
 Granger CWJ (1993) Strategies for modelling nonlinear timeseries relationships. Econ Rec 69(206):233–238
 Hansen JV, Nelson RD (2003) Forecasting and recombining timeseries components by using neural networks. J Oper Res Soc 54(3):307–317
 Hamzacebi C (2008) Improving artificial neural networks’ performance in seasonal time series forecasting. Inf Sci 178:4550–4559
 Hamzacebi C, Akay D, Kutay F (2009) Comparison of direct and iterative artificial neural network forecast approaches in multiperiodic time series forecasting. Expert Syst Appl 36(2):3839–3844
 Hill T, O’Connor M, Remus W (1996) Neural network models for time series forecasts. Manag Sci 42:1082–1092
 Hippert HS, Pedreira CE, Souza RC (2001) Neural networks for shortterm load forecasting: a review and evaluation. IEEE Trans Power Syst 16(1):44–55
 Hippert HS, Bunn DW, Souza RC (2005) Large neural networks for electricity load forecasting: are they overfitted? Int J Forecasting 21(3):425–434
 Hoptroff RG (1993) The principles and practice of time series forecasting and business modeling using neural networks. Neural Comput Appl 1:59–66
 Huskent M, Stagge P (2003) Recurrent neural networks for time series classification. Neurocomputing 50:223–235
 Hwang HB (2001) Insights into neuralnetwork forecasting of time series corresponding to ARMA (p,q) structures. Omega 29:273–289
 Hylleberg S (1992) General introduction. In: Hylleberg S (ed) Modelling seasonality. Oxford University Press, Oxford, pp 3–14
 Hylleberg S (1994) Modelling seasonal variation. In: Hargreaves CP (ed) Nonstationary time series analysis and cointegration. Oxford University Press, Oxford, pp 153–178
 Ittig PT (1997) A seasonal index for business. Decis Sci 28(2):335–355
 Jain A, Kumar AM (2007) Hybrid neural network models for hydrologic time series forecasting. Appl Soft Comput 7:585–592
 Jiang X, Adeli H (2005) Dynamic wavelet neural network model for traffic flow forecasting, J Transportation Eng 131(10):771–779
 Kang IB (2003) Multiperiod forecasting using different models for different horizons: an application to U.S. economic time series data. Int J Forecasting 19:387–400
 Kaastra I, Boyd M (1996) Designing a neural network for forecasting financial and economic time series. Neurocomputing 10:215–236
 Kermanshahi B (1998) Recurrent neural network for forecasting next 10 years loads of nine Japanese utilities. Neurocomoputing 23:125–133
 Khashei M, Hejazi SR, Bijari M (2008) A new hybrid artificial neural networks and fuzzy regression model for time series forecasting. Fuzzy Sets Syst 159:769–786
 Khotanzad A, AfkhamiRohani R, Lu TL, Abaye A, Davis M, Maratukulam DJ (1997) ANNSTLF—A neuralnetworkbased electric load forecasting system. IEEE Trans Neural Netw 8(4):835–846
 Kline DM (2004) Methods for multistep time series forecasting with neural networks. In: Zhang GP (ed) Neural networks for business forecasting. Idea Group, Hershey, PA, pp 226–250
 Kolarik T, Rudorfer G (1994) Time series forecasting using neural networks. APL Quote Quad 25:86–94
 Krycha KA, Wagner U (1999) Applications of artificial neural networks in management science: a survey. J Retailing Consum Serv 6:185–203
 Kuan CM, Liu T (1995) Forecasting exchange rates using feedforward and recurrent neural networks. J Appl Economet 10:347–364
 Lapedes A, Farber R (1987) Nonlinear signal processing using neural networks: Prediction and system modeling. Technical Report LAUR872662, Los Alamos National Laboratory, Los Alamos, NM
 LeBaron B, Weigend AS (1998) A bootstrap evaluation of the effect of data splitting on financial time series. IEEE Trans Neural Netw 9(1):213–220
 Lennon B, Montague GA, Frith AM, Gent C, Bevan V (2001) Industrial applications of neural networks—An investigation. J Process Control 11:497–507
 Liang F (2005) Bayesian neural networks for nonlinear time series forecasting. Stat Comput 15:13–29
 Lin JL, Granger CWJ (1994) Forecasting from nonlinear models in practice. J Forecasting 13:1–9
 Liu Y, Yao X (1999) Ensemble learning via negative correlation. Neural Netw 12:1399–1404
 Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resource variables: a review of modeling issues and applications. Environ Model Software 15:101–124
 Makridakis S, Anderson A, Carbone R, Fildes R, Hibdon M, Lewandowski R, Newton J, Parzen E, Winkler R (1982) The accuracy of extrapolation (time series) methods: results of a forecasting competition. J Forecasting 1(2):111–153
 Mandic D, Chambers J (2001) Recurrent neural networks for prediction: learning algorithms, architectures and stability. Wiley, Chichester, UK
 Marcellino M, Stock JH, Watson MW (2006) A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series. J Economet 135:499–526
 Medeiros MC, Pedreira CE (2001) What are the effects of forecasting linear time series with neural networks? Eng Intell Syst 4:237–424
 Medeiros MC, Veiga A (2000) A hybrid linearneural model for time series forecasting. IEEE Trans Neural Netw 11(6):1402–1412
 Medeiros MC, Terasvirta T, Rech G (2006) Building neural network models for time series: a statistical approach. J Forecasting 25:49–75
 Menezes JMP, Barreto GA (2008) Longterm time series prediction with the NARX network: an empirical evaluation. Neurocomputing 71:3335–3343
 Mishra AK, Desai VR (2006) Drought forecasting using feedforward recursive neural network. Ecol Model 198:127–138
 Nakamura E (2005) Inflation forecasting using a neural network. Econ Lett 86:373–378
 Nelson M, Hill T, Remus T, O’Connor M (1999) Time series forecasting using neural networks: should the data be deseasonalized first? J Forecasting 18:359–367
 Palmer A, Montano JJ, Sese A (2006) Designing an artificial neural network for forecasting tourism time series. Tourism Manag 27:781–790
 Parisi A, Parisi F, Díaz D (2008) Forecasting gold price changes: rolling and recursive neural network models. J Multinational Financial Manag 18(5):477–487
 Parlos AG, Rais OT, Atiya AF (2000) Multistepahead prediction using dynamic recurrent neural networks. Neural Netw 13:765–786
 Pelikan E, de Groot C, Wurtz D (1992) Power consumption in WestBohemia: improved forecasts with decorrelating connectionist networks. Neural Netw World 2(6):701–712
 Pino P, Parreno J, Gomez A, Priore P (2008) Forecasting nextday price of electricity in the Spanish energy market using artificial neural networks. Eng Appl Artif Intell 21:53–62
 Poli I, Jones DR (1994) A neural net model for prediction. J Am Stat Assoc 89:117–121
 Qi M, Zhang GP (2001) An investigation of model selection criteria for neural network time series forecasting. Eur J Oper Res 132:666–680
 Qi M, Zhang GP (2008) Trend timeseries modeling and forecasting with neural networks. IEEE Trans Neural Netw 19(5):808–816
 Reed R (1993) Pruning algorithms—A survey. IEEE Trans Neural Netw 4(5):740–747
 Remus W, O’Connor M (2001) Neural networks for time series forecasting. In: Armstrong JS (ed) Principles of forecasting: a handbook for researchers and practitioners. Kluwer, Norwell, MA, pp 245–256
 Ripley BD (1996) Pattern recognition and neural networks. Cambridge University Press, Cambridge
 Rumelhart DE, McClelland JL, PDP Research Group (1986) Parallel distributed processing: explorations in the microstructure of cognition, Foundations, vol 1. MIT Press, Cambridge, MA
 Sharda R, Patil RB (1992) Connectionist approach to time series prediction: an empirical test. J Intell Manufacturing 3:317–323
 Sharkey CJ (1996) On combining artificial neural nets. Connect Sci 8:299–314
 Sharkey CJ, Sharkey EN (1997) Combining diverse neural nets. Knowledge Eng Rev 12(3):231–247
 Smith M (1993) Neural networks for statistical modeling. Van Nostrand Reinhold, New York
 Stoica P, Nehorai A (1989) On multistep prediction errors methods for time series models. J Forecasting 13:109–131
 Swanson NR, White H (1995) A modelselection approach to assessing the information in the term structure using linear models and artificial neural networks. J Bus Econ Stat 13:265–275
 Tiao GC, Tsay RS (1994) Some advances in nonlinear and adaptive modeling in timeseries. J Forecasting 13:109–131
 Teräsvirta T, van Dijk D, Medeiros MC (2005) Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: a reexamination. Int J Forecasting 21(4):755–774
 Vellido A, Lisboa PJG, Vaughan J (1999) Neural networks in business: a survey of applications (1992–1998). Expert Syst Appl 17:51–70
 Vermaak J, Botha EC (1998) Recurrent neural networks for shortterm load forecasting. IEEE Trans Power Syst 13(1):126–132
 Wang YH (2009) Nonlinear neural network forecasting model for stock index option price: hybrid GJRGARCH approach. Expert Syst Appl 36(1):564–570
 Wedding K II, Cios JK (1996) Time series forecasting by combining RBF networks, certainty factors, and the BoxJenkins model. Neurocomputing 10:149–168
 Weigend AS, Gershenfeld NA (1994) Time series prediction: forecasting the future and understanding the past. AddisonWesley, Reading, MA
 Weigend AS, Huberman BA, Rumelhart DE (1990) Predicting the future: a connectionist approach. Int J Neural Syst 1:193–209
 Weigend AS, Huberman BA, Rumelhart DE (1992) Predicting sunspots and exchange rates with connectionist networks. In: Casdagli M, Eubank S (eds) Nonlinear modeling and forecasting. AddisonWesley, Redwood City, CA, pp 395–432
 Werbos P (1974) Beyond regression: new tools for prediction and analysis in the behavioral sciences. Ph.D. thesis, Harvard University
 Wichard J, Ogorzalek M (2007) Time series prediction with ensemble models applied to the CATS benchmark. Neurocomputing 70:2371–2378
 Wong BK, Selvi Y (1998) Neural network applications in finance: a review and analysis of literature (1990–1996). Inf Manag 34:129–139
 Wong BK, Lai VS, Lam J (2000) A bibliography of neural network business applications research: 1994–1998. Comput Oper Res 27:1045–1076
 Xiao Z, Ye SJ, Zhong B, Sun CX (2009) BP neural network with rough set for short term load forecasting. Expert Syst Appl 36(1):273–279
 Zhang G, Patuwo EP, Hu MY (1998) Forecasting with artificial neural networks: the state of the art. Int J Forecasting 14:35–62
 Zhang GP (2001) An investigation of neural networks for linear timeseries forecasting. Comput Oper Res 28:1183–1202
 Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50:159–175
 Zhang GP (2004) Neural networks in business forecasting. Idea Group, Hershey, PA
 Zhang GP (2007a) Avoiding pitfalls in neural network research. IEEE Trans Syst Man and Cybern 37:3–16
 Zhang GP (2007b) A neural network ensemble method with jittered training data for time series forecasting. Inf Sci 177:5329–5346
 Zhang GP, Berardi LV (2001) Time series forecasting with neural network ensembles: An application for exchange rate prediction. J Oper Res Soc 52(6):652–664
 Zhang GP, Kline D (2007) Quarterly timeseries forecasting with neural networks. IEEE Trans Neural Netw 18(6):1800–1814
 Zhang GP, Qi M (2002) Predicting consumer retail sales using neural networks. In: Smith K, Gupta J (eds) Neural networks in business: techniques and applications. Idea Group, Hershey, PA, pp 26–40
 Zhang GP, Qi M (2005) Neural network forecasting for seasonal and trend time series. Eur J Oper Res 160(2):501–514
 Zhang GP, Patuwo EP, Hu MY (2001) A simulation study of artificial neural networks for nonlinear time series forecasting. Comput Oper Res 28:381–396
 Zhang YQ, Wan X (2007) Statistical fuzzy interval neural networks for currency exchange rate time series prediction. Appl Soft Comput 7:1149–1156
 Zou HF, Xia GP, Yang FT, Wang HY (2007) An investigation and comparison of artificial neural network and time series models for Chinese food grain price forecasting. Neurocomputing 70:2913–2923
 Title
 Neural Networks for TimeSeries Forecasting
 Reference Work Title
 Handbook of Natural Computing
 Pages
 pp 461477
 Copyright
 2012
 DOI
 10.1007/9783540929109_14
 Print ISBN
 9783540929093
 Online ISBN
 9783540929109
 Publisher
 Springer Berlin Heidelberg
 Copyright Holder
 SpringerVerlag Berlin Heidelberg
 Additional Links
 Topics
 Industry Sectors
 eBook Packages
 Editors

 Grzegorz Rozenberg ^{(251)} ^{(252)}
 Thomas Bäck ^{(253)}
 Joost N. Kok ^{(254)}
 Editor Affiliations

 251. LIACS, Leiden University
 252. Computer Science Department, University of Colorado
 253. LIACS, Leiden University
 254. LIACS, Leiden University
 Authors

 G. Peter Zhang ^{(00141)}
 Author Affiliations

 00141. Department of Managerial Sciences, Georgia State University, Atlanta, GA, USA
Continue reading...
To view the rest of this content please follow the download PDF link above.