S. Pratiher, S. Mukhopadhyay, Ritwik Barman, S. Pratiher, S. Dey, S. Banerjee, P. Panigrahi
{"title":"基于ARIMA的降水温度动态预报的递归量化","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":"{\"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}","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}
Recurrence quantification & ARIMA based forecasting of rainfall-temperature dynamics
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.