{"title":"A hybrid method for forecasting trend and seasonal time series","authors":"Doan Ngoc Bao, Ngo Duy Khanh Vy, D. T. Anh","doi":"10.1109/RIVF.2013.6719894","DOIUrl":null,"url":null,"abstract":"Forecasting of time series that have trend and seasonal variations remains an important problem for forecasters. In this work, a hybrid method which combines Winters' exponential smoothing method and neural network is proposed for forecasting seasonal and trend time series. The proposed method aims to integrate the linear characteristics of an exponential smoothing model and nonlinear characteristics of neural network to create a more effective model for time series forecasting. Experimental results show that the hybrid method outperforms neural network model in forecasting seasonal and trend time series.","PeriodicalId":121216,"journal":{"name":"The 2013 RIVF International Conference on Computing & Communication Technologies - Research, Innovation, and Vision for Future (RIVF)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 2013 RIVF International Conference on Computing & Communication Technologies - Research, Innovation, and Vision for Future (RIVF)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RIVF.2013.6719894","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
Forecasting of time series that have trend and seasonal variations remains an important problem for forecasters. In this work, a hybrid method which combines Winters' exponential smoothing method and neural network is proposed for forecasting seasonal and trend time series. The proposed method aims to integrate the linear characteristics of an exponential smoothing model and nonlinear characteristics of neural network to create a more effective model for time series forecasting. Experimental results show that the hybrid method outperforms neural network model in forecasting seasonal and trend time series.