Forecasting of Annual Rainfall using Fuzzy Logic Interval Based Partitioning in Different Intervals
DOI:
https://doi.org/10.23755/rm.v45i0.1006Keywords:
Mean Square Error, Fuzzy time series, Average Forecast Error Rate.Abstract
Fuzzy time series models have been proposed by many researchers around the world for rainfall forecasting, but the forecasting has not been as accurate as existing methods. Frequency density or ratio-based segmentation methods have been used to represent discourse segmentation. In this paper, to make such predictions, we used interval-based segmentation as the discourse segmentation and the urban mean rainfall in the Trichy district as the discourse universe. Fuzzy models are used for forecasting in many fields such as admissions prediction, stock price analysis, agricultural production, horticultural production, marine production, weather forecasting, and more.
References
Adesh Kumar Pandey, Sinha A.K,Srivastava V.K,A Comparative Study of Neural-Network&FuzzyTimeSeriesForecastingTechniques
Askar Choudhury, James Jones, CROP YIELD PREDICTION USINGTIMESERIESMODELS,JournalofEconomicandEconomicEducationResearch,Volume15,Number3,(2014).
BinduGarg,RohitGarg, EnhancedAccuracy ofFuzzyTimeSeriesModel using Ordered Weighted Aggregation, Applied Soft computing,Elsevier, Volume.8,Pg:265-280,(2016).
Chen S. M, Forecasting Enrollments based on High Order Fuzzy TimeSeries,IntlJournalofCyberneticsandSystems,Vol.33,Pg:1-16,(2010).
Chen S.M.,Forecasting Enrollments based on Fuzzy Time Series,Fuzzy Sets and Systems, Vol. 81,Pg.:311-319,(1996).
Garg B., M. Beg M. S., A. Q. Ansari, Employing OWA to OptimizeFuzzyPredicator,WorldConferenceonSoftComputing(WConSC2011),SanFranciscoStateUniversity,USA,Pg.:205-211,(2011).
Garg B., M. Beg M. S., and. Ansari A. Q, OWA based Fuzzy TimeSeriesForecastingModel,WorldConferenceonSoftComputing,Berkeley,SanFrancisco,CA,and Pg.:141-177,May23-26, (2011).
Garg B., M. Beg M. S., Ansari A.Q.and Imran B.M., Fuzzy Time Series Prediction Model,CommunicationsinComputerandInformationScience, Springer–Verlag Berlin Heidelberg, ISBN978-3-642-19423-8,Vol. 141,Pg.:126-137,(2011).
Garg B., M.. Beg M. S and. Ansari A. Q, A New Computational FuzzyTimeSeriesModeltoForecastNumberofOutpatientVisits,―Proc.31stAnnualConferenceoftheNorthAmericanFuzzyInformationProcessingSociety(NAFIPS2012),UniversityofCaliforniaatBerkeley, USA,Pg:1-6, August6-8,(2012).
Garg B., M.. Beg M. S and. Ansari A. Q, Enhanced Accuracy of FuzzyTimeSeriesPredictorusingGeneticAlgorithm,ThirdIEEEWorldCongress on Nature and Biologically Inspired Computing (NaBIC2011),Pg:273-278,Spain,(2011).
Garg B., M.. Beg M. S, Ansari A.Q.and. Imran B. M, Soft ComputingModel to PredictAverageLengthof Stay of Patient,, CommunicationsinComputerandInformationScience,SpringerVerlagBerlinHeidelberg,ISBN978-3-642-19423-8,Vol.141,Pg.:221–232,(2011).
Garg B.,M. Beg M. S and. Ansari A. Q, Employing Genetic Algorithm to Optimize OWA-Fuzzy Forecasting Model,Third IEEE World Congress on Nature and Biologically Inspired Computing (NaBIC2011),Pg.:285-290,Spain,(2011).
Huarng K, Heuristic Models of Fuzzy Time Series for Forecasting;FuzzySetsandSystems,Vol.123, Pg.:369-386,(2002).
Huarng K.H,. Yu T.H.K and. Hsu Y.W, a Multivariate Heuristic Modelfor Forecasting, IEEE Transactions on Systems, Man, and Cybernetics,Vol. 37,Pg.:836–846,(2007).
Huarng. K, Effective Lengths of Intervals to Improve Forecasting inFuzzy Time Series,Fuzzy Sets and Systems, Vol. 12, Pg: 387-394,(2001).
Hwang J. R.,. Chen S. M, Lee C. H., Handling Forecasting Problemsusing Fuzzy Time Series, Fuzzy Sets and Systems, Vol. 100, Pg.: 217-228,(1998).
Lee C.H.L, Lin A.and Chen W.S., Pattern Discovery of Fuzzy TimeSeries for Financial Prediction, IEEE Transaction on Knowledge DataEngineering, Vol.18 Pg.:613–625,(2006).
Lee L. W., Wang L. W.,. Chen S. M, Handling Forecasting Problemsbased on Two-Factors High-Order Time Series, IEEE Transactions onFuzzySystems, Vol. 14,Pg:.468-477,(2006).
Li H., Kozma R., A Dynamic Neural Network Method For Time SeriesPrediction using the KIII Model, Proceedings of the 2003 InternationalJointConferenceon NeuralNetworks, Pg:347-352, (2003).
Narendrakumar, Sachin Ahuja, Vipin Kumar, Amit Kumar, Fuzzy timeseriesforecastingofwheatproduction,InternationalJournalonComputerScienceandEngineering,Vol.02,Pg:635-640,(2010).
PankajKumar,CropYieldForecastingbyAdaptiveNeuroFuzzyInferenceSystem,MathematicalTheoryandModeling,ISSN2225-0522, Vol.1,No.3,(2011).
Qiu W.,Liu X. andLi H.,Ageneralizedmethodforforecastingbasedon fuzzy time series, Expert Systems with Applications, Vol. 38, Pg:10446-1045, (2011).
SachinKumar .Dr,NarendraKumar,ANovelMethodforRiceProduction Forecasting Using Fuzzy Time Series, International JournalofComputerScience Issues, Vol. 9,Issue6, No2, (2012).
Sachin Kumar .Dr, Narendra Kumar, Two Factor Fuzzy Time SeriesModelforRiceForecasting,InternationalJournalofComputer&MathematicalSciences,ISSN2347–8527,Volume4,Issue1,(2015).
Singh S.R,A Robust Method of Forecasting based on Fuzzy Time Series, International Journal of Applied Mathematics and Computations,Vol. 188,Pg:472-484,(2007).
Song .Q, Chissom B. S., Fuzzy Time Series and its Models, Fuzzy Setsand Systems, Vol. 54,Pg.:269-277,(1993).
Song Q, a Note on Fuzzy Time Series Model Selection with SampleAutocorrelation Functions, an International Journal of Cybernetics andSystems,Vol.34, andPg.:93-107,(2003).
Song Q,Chissom B. S., Forecasting Enrollments with Fuzzy Time Series: PartII, FuzzySets andSystems, Vol. 62,Pg.:1-8, (1994).
VikasLamba,Dhaka V.S.,Wheat Yield Prediction Using Artificial Neural Network and Crop Prediction Techniques, International Journal for Research in Applied Science and Engineering Technology, Vol. 2Issue IX,ISSN:2321-9653,(2014).
Yolcu U., Egrioglu E., Vedide R. Uslu R., Basaran M. A., Aladag C. H.,A new approach for determining the length of intervals for fuzzy timeseries,Applied Soft Computing, Vol.9, Pg.:647-651, (2009).
Downloads
Published
Issue
Section
License
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge. The journal is committed to real and immediate open access for academic work. All of the journal articles are free to access immediately from the date of publication. There are no charge for readers to download articles and reviews for their own scholarly use. Benefits of open access for authors, include:
- Free access for all users worldwide
- Increased visibility and readership
- Rapid publication
- No spatial constraints
- Authors can share their research papers on their personal web-pages (i.e. ResearchGate, etc.)
Ratio Mathematica publishes open access articles under the terms of the Creative Commons Attribution (CC BY) License. The Creative Commons Attribution License (CC-BY) allows users to copy, distribute and transmit an article, adapt the article and make commercial use of the article. The CC BY license permits commercial and non-commercial re-use of an open access article, as long as the author is properly attributed. Copyright on any research article published by Ratio Mathematica is retained by the author(s). Authors grant Ratio Mathematica a license to publish the article and identify itself as the original publisher. Authors also grant any third party the right to use the article freely as long as its original authors, citation details and publisher are identified.
For further information visit: https://creativecommons.org/licenses/by/3.0/. The following logo will appear in any paper that will be published from the volume number 31/2016 onwards.
