S. Pratiher, S. Mukhopadhyay, Ritwik Barman, S. Pratiher, S. Dey, S. Banerjee, P. Panigrahi
{"title":"Recurrence quantification & ARIMA based forecasting of rainfall-temperature dynamics","authors":"S. Pratiher, S. Mukhopadhyay, Ritwik Barman, S. Pratiher, S. Dey, S. Banerjee, P. Panigrahi","doi":"10.1109/ICSPCOM.2016.7980630","DOIUrl":null,"url":null,"abstract":"Recurrence quantification analysis (RQA) deals with the nonlinear and non-stationarity of dynamical systems and quantifies the recurrence number and duration of phase space trajectory. In this paper, RQA has been used to analyze the phase transitions of rainfall and temperature fluctuations as well as their transient interdependencies, of places in and around districts of West Bengal, India. This is followed by a unit root nonstationary linear forecasting using ARIMA method. Mean square error of −0.497, validates the efficacy of the proposed methodology in forecasting.","PeriodicalId":213713,"journal":{"name":"2016 International Conference on Signal Processing and Communication (ICSC)","volume":"11 11","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Signal Processing and Communication (ICSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSPCOM.2016.7980630","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
Recurrence quantification analysis (RQA) deals with the nonlinear and non-stationarity of dynamical systems and quantifies the recurrence number and duration of phase space trajectory. In this paper, RQA has been used to analyze the phase transitions of rainfall and temperature fluctuations as well as their transient interdependencies, of places in and around districts of West Bengal, India. This is followed by a unit root nonstationary linear forecasting using ARIMA method. Mean square error of −0.497, validates the efficacy of the proposed methodology in forecasting.