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A Review of Soft Computing Techniques for Time Series Forecasting

Affiliations

  • Department of Information Technology, CSPIT, CHARUSAT Campus, Anand, Gujarat − 388421, India
  • Department of Information Technology, A.D. Patel Institute of Technology, New V.V. Nagar, Gujarat − 388121, India

Abstract


Objectives: This paper gives a brief survey analysis on time series forecasting models developed so far and how soft computing techniques are becoming popular for time series forecasting. Statistical Analysis: This paper gives an introductory study on time series models develop in literature so far. First all traditional models were developed among them ARIMA became popular but only for linear data. Then after Soft Computing models came into picture like ANN and it was proved to be best for nonlinear forecasting. But none of the individual models are capable to handle all types of datasets as real data is a mixture of linear and nonlinear both. So there arises a need for hybridization. Findings: Many traditional methods have been developed since last few decades for time series forecasting, however their performance is not up to the mark till today. Recent trends have proven that soft computing techniques like neural network, support vector machine are good alternatives to conventional methods. The accuracy of time series model is very important and difficult task to achieve, and individual models are not able to perform well for all types of time series data. So, hybridization of models is better to achieve good accurate forecasting results. Application/Improvements: Implementation of a hybrid model using soft computing techniques which can give better accurate results as real data are made linear and nonlinear both. This hybrid model can be used in applications like weather forecasting, exchange rate forecasting, etc.

Keywords

ARIMA, Artificial Neural Network, Hybrid Model, Soft Computing Techniques, Time Series, Time Series Forecasting

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References


  • Li CS, Chiang TW. Complex Neurofuzzy ARIMA Forecasting-A New Approach using Complex Fuzzy Sets, IEEE Trans Fuzzy Syst. 2013; 21(3):567−84. DOI: 10.1109/tfuzz.2012.2226890.
  • Bautu E, Barbulescu A. Forecasting Meteorological Time Series using Soft Computing Methods: An Empirical Study, Appl. Math. 2013; 7(4):1297−306.
  • Adhikari R, Agrawal RK. An Introductory Study on Time Series Modeling and Forecasting, arXiv Prepr arXiv13026613. 2013.
  • Meryem O, Ismail J, Mohammed E-M. A Comparative Study of Predictive Algorithms for Time Series Forecasting. In: Information Science and Technology (CIST), 2014 Third IEEE International Colloquium; 2014. p. 68−73.
  • Khashei M, Bijari M. An Artificial Neural Network (p,d,q) Model for Timeseries Forecasting, Expert Syst. Appl. 2010; 37(1):479−89. Doi: 10.1016/j.eswa.2009.05.044.
  • Khashei M, Bijari M. A Novel Hybridization of Artificial Neural Networks and ARIMA Models for Time Series Forecasting, Appl. Soft. Comput. J. 2011; 11(2):2664−75. DOI: 10.1016/j.asoc.2010.10.015.
  • Khashei M, Bijari M. A New Class of Hybrid Models for Time Series Forecasting, Expert Syst. Appl. 2012; 39(4):4344−57.
  • De Gooijer JG, Hyndman RJ. 25 Years of Time Series Forecasting, Int. J. Forecast. 2006; 22(3):443−73. DOI: 10.1016/j.ijforecast.2006.01.001.
  • Babu CN, Reddy BE. A Moving-Average Filter based Hybrid ARIMA–ANN Model for Forecasting Time Series Data, Appl. Soft. Comput. 2014; 23:27−38. DOI: 10.1016/j.asoc.2014.05.028.
  • Hyndman RJ, Athanasopoulos G. Forecasting: Principles and Practice. OTexts; 2014.
  • Montgomery DC, Jennings CL, Kulahci M. Introduction to Time Series Analysis and Forecasting. John Wiley & Sons; 2015.
  • Wang L, Zou H, Su J, Li L, Chaudhry S. An ARIMA-ANN Hybrid Model for Time Series Forecasting, Syst. Res. Behav. Sci. 2013; 30(3):244−59. DOI: 10.1002/sres.2179.
  • Mandal SN, Choudhury JP, Chaudhuri SRB, De D. Soft Computing Approach in Prediction of a Time Series Data, J Theor. Appl. Inf. Technol. 2008; 8(3):1131−41.
  • Rojas I, Palomares H. Soft-Computing Techniques for Time Series Forecasting. In: Proc. of the European Symposium on Artificial Neural Networks; 2004. p. 93−102.
  • Agami N, Atiya A, Saleh M, El-Shishiny H. A Neural Network based Dynamic Forecasting Model for Trend Impact Analysis, Technol. Forecast Soc. Change. 2009; 76(7):952−62.
  • Chen Y, Yang B, Dong J, Abraham A. Time-Series Forecasting using Flexible Neural Tree Model, Inf. Sci. (Ny). 2005; 174(3):219−35.
  • Piotrowski AP, Napiorkowski MJ, Napiorkowski JJ, Osuch M. Comparing Various Artificial Neural Network Types for Water Temperature Prediction in Rivers, J. Hydrol. 2015; 529:302−15.
  • Samsudin R. A Comparison of Time Series Forecasting using Support Vector Machine and Artificial Neural Network Model, J. Appl. Sci. 2010; 10(11):950−58.
  • Gunn SR. Support Vector Machines for Classification and Regression, ISIS Technical Report. 1998 May 10. p. 14.
  • Sapankevych N, Sankar R. Time Series Prediction using Support Vector Machines: A Survey, IEEE Comput. Intell. Mag. 2009; 4(May):24−38. DOI: 10.1109/MCI.2009.932254.
  • Xiang L, Tang G, Zhang C. Simulation of Time Series Prediction based on Hybrid Support Vector Regression. In: Natural Computation; 2008. ICNC’08. Fourth International Conference. 2008; 2:167−71.
  • Xiang L, Zhu Y, Tang G. A Hybrid Support Vector Regression for Time Series Forecasting. In: Software Engineering, 2009. WCSE’09. WRI World Congress. 2009; 4:161−65.
  • Chen K-Y. Forecasting Systems Reliability based on Support Vector Regression with Genetic Algorithms, Reliab. Eng. Syst. Saf. 2007; 92(4):423−32.
  • Zhang X, Zhang T, Young AA, Li X. Applications and Comparisons of Four Time Series Models in Epidemiological Surveillance Data, PLoS One. 2014; 9(2):e88075. DOI: 10.1371/journal.pone.0088075.
  • Zhang GP. Time Series Forecasting using a Hybrid ARIMA and Neural Network Model, Neurocomputing. 2003; 50:159−75.
  • Bao DN, Vy NDK, Anh DT. A Hybrid Method for Forecasting Trend and Seasonal Time Series. In: Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), 2013 IEEE RIVF International Conference; 2013. p. 203−08.
  • Reyhani R, Others. A Heuristic Method for Forecasting Chaotic Time Series based on Economic Variables. In: Digital Information Management (ICDIM), 2011 Sixth International Conference; 2011. p. 300−04.
  • Yolcu U, Egrioglu E, Aladag CH. A New Linear and Nonlinear Artificial Neural Network Model for Time Series Forecasting, Decis. Support Syst. 2013; 54(3):1340−47. DOI: 10.1016/j.dss.2012.12.006.
  • Vijayalaksmi DP, Babu KSJ. Water Supply System Demand Forecasting Using Adaptive Neuro-fuzzy Inference System. Aquat. Procedia. 2015; 4:950−56.
  • Zounemat-Kermani M, Teshnehlab M. Using Adaptive Neuro-Fuzzy Inference System for Hydrological Time Series Prediction, Appl. Soft. Comput. 2008; 8(2):928−36.

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