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Reseach Article

A Study on Forecaster Model using Time Series Data

by Ashwini N., Rajshekar Patil M.
Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 7 - Number 2
Year of Publication: 2017
Authors: Ashwini N., Rajshekar Patil M.
10.5120/cae2017652604

Ashwini N., Rajshekar Patil M. . A Study on Forecaster Model using Time Series Data. Communications on Applied Electronics. 7, 2 ( May 2017), 34-39. DOI=10.5120/cae2017652604

@article{ 10.5120/cae2017652604,
author = { Ashwini N., Rajshekar Patil M. },
title = { A Study on Forecaster Model using Time Series Data },
journal = { Communications on Applied Electronics },
issue_date = { May 2017 },
volume = { 7 },
number = { 2 },
month = { May },
year = { 2017 },
issn = { 2394-4714 },
pages = { 34-39 },
numpages = {9},
url = { https://www.caeaccess.org/archives/volume7/number2/739-2017652604/ },
doi = { 10.5120/cae2017652604 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-04T20:01:20.345217+05:30
%A Ashwini N.
%A Rajshekar Patil M.
%T A Study on Forecaster Model using Time Series Data
%J Communications on Applied Electronics
%@ 2394-4714
%V 7
%N 2
%P 34-39
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Many physical and artificial phenomena can be described by time series. The prediction of any such phenomenon could be complex and interesting. The ability to forecast the future is mainly based on only past data, which leads to strategic advantages and will be key to success in organizations. Time series forecasting allows the modeling of complex systems as black-boxes, being a focus of attention in several research arenas. There are several methods for time series data which mainly depends whether the data is linear or nonlinear. In this paper a survey on the forecasting method based on the different types of the data presented. This survey will mainly concentrate based on neural network, evolutionary computation etc. in solution development of forecasting models and rules, continued with hybrid forecaster mainly.

References
  1. Zhenfeng Shao, Yuan Zhang and Weiqi Zhou, "Long-term monitoring of the urban impervious surface mapping using time series Landsat imagery: A 23-year case study of the city of Wuhan in China" 2016 4th International Workshop on Earth Observation and Remote Sensing Applications (EORSA), Guangzhou, 2016:212-216. (doi:10.1109/EORSA.2016.7552799)
  2. Richard John M Buendia and Geoffrey A Solano, “A Disease Outbreak Detection System using Autoregressive Moving Average in Time Series Analysis”, published in 6th International Conference on Information, Intelligence, Systems and Applications(IISA),Corfu,2015;1-5. (doi: 10.1109/IISA.2015.7388087.)
  3. Ashwini N and Bhavya G, “Using Mining and Intelligent Approach for Time Series Forecasting Problems” ,International Journal of Advanced Research in Computer Science and Software Engineering, November 2013;3(11);1567-1571.
  4. Samuel H. Huddleston and Donald E. Brown,”Using Discrete Event Simulation To Evaluate Time Series Forecasting Methods For Security Applications”, Proceedings of the Winter Simulation Conference,2013;2772-2783.
  5. Mehdi K and Mehdi B, “A novel hybridization of artificial neural networks and ARIMA models for time series forecasting”, Applied Soft Computing, 2011;11.
  6. Lee Y and Tong L , “Forecasting time series using a metho3dology based on autoregressive integrated moving average and genetic programming”, Knowledge-Based Systems,2011; 24; 66-72.
  7. Theodosiou M, “Disaggregation and aggregation of time series components: a hybrid forecasting approach using generalized regression neural networks and the theta method”, Neurocomputing,2011;20;896-905.
  8. Azadeh, A. and Faiz, Z. , “A meta-heuristic framework for forecasting household electricity consumption”, Applied Soft Computing, 2011;11; 614-20.
  9. Lee Y and Tong L , “Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming”, Knowledge-Based Systems, 2011;24; 66-72.
  10. Araujo. R., “A quantum-inspired evolutionary hybrid intelligent approach for stock market prediction”, International Journal of Intelligent Computing and Cybernetics, 2010;3; 24-54.
  11. Chen, S. and Chen, J., “Forecasting container throughputs at ports using genetic programming”, Expert Systems with Applications, 2010;37;2054-8.
  12. Chern, C., Ao, I., Wu, L. and Kung, L., “Designing a decision-support system for new product sales forecasting”, Expert Systems with Applications, 2010; 37;1654-65.
  13. Dilip, P. , “Improved forecasting of time series data of real system using genetic programming”, GECCO ’10 Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, Portland, OR, USA, 2010;1;977-8.
  14. Chen, A. and Leung, M., “A Bayesian vector error correction model for forecasting exchange rates”, Computers & Operations Research, 2003;30;887-900.
  15. McMillan, D.G. , “Nonlinear predictability of stock market returns: evidence from nonparametric and threshold models”, International Review of Economics and Finance, 2001;10;353-68;
  16. Chiraphadhanakul, S., Dangprasert, P. and Avatchanakorn, V., “Genetic algorithms in forecasting commercial banks deposit”, Proceedings of the IEEE International Conference on Intelligent Processing Systems, Beijing, China, 1997;1;557-65.
  17. Back, T., “Evolutionary Algorithms in Theory and Practice: Evolution Strategies,Evolutionary Programming, and Genetic Algorithms”, Oxford University Press,New York, NY, 1996.
  18. Chambers, L. (Ed.), “Practical Handbook of Genetic Algorithms: Applications”, CRC Press,Boca Raton, FL, 1995.
  19. Clements, M. and Hendry, D. , “Forecasting in cointegrated systems”, Journal of Applied Econometrics, 1995;10;127-46.
  20. Fogel, L., Angeline, P. and Fogel, D. , “An evolutionary programming approach to self-adaptation on finite state machines”, Proceedings of the 4th Annual Conference onEvolutionary Programming, San Diego, CA, USA,1995;1; 355-65.
  21. Baille, R. and Bollerslev, T. , “Cointegration, fractional cointegration, and exchange rate dynamics”, Journal of Finance, 1994;49;737-45.
  22. Cheung, Y. and Lai, K., “A fractional cointegration analysis of purchasing power parity”, Journal of Business and Economic Statistics, 1993;11;103-12.
  23. Akgiray, V., “Conditional heteroskedasticity in time series and stock returns: evidence and forecasts”, Journal of Business,1989;62;55-80.
  24. Bollerslev, T. , “Generalized autoregressive conditional heteroskedasticity”, Journal of Econometrics, 1986;31; 307-27.
  25. Engle, R., “Autoregressive conditional heteroskedasticity with estimates of the variance of UK inflation”, Econometrica, 1982;50;987-1008.
  26. Bass, F. , “A new product growth model for consumer durables”, Management Science,1969;15;215-27.
  27. https://www.google.co.in/?gfe_rd=cr&ei=AMqKWNvyEceL8Qewr7zIAg#q=survey+on+Statistical+time+series+forecasting+methods
Index Terms

Computer Science
Information Sciences

Keywords

Moving Average(MA) Autoregressive(AR) Neural Networks Genetic algorithms Time series forecasting Autoregressive Integrated Moving Area(ARIMA) methods Generalized autoregressive conditionally heteroskedastic(GARCH) methods Artificial Neural Networks(ANN)