{"title":"每月布伦特原油价格预测使用人工神经网络和危机指数","authors":"A. Alizadeh, K. Mafinezhad","doi":"10.1109/ICEIE.2010.5559818","DOIUrl":null,"url":null,"abstract":"The volatility of the oil future price is extremely complex, therefore an accurate forecasting on oil price is an important and challenging topic. This paper presents a GRNN forecasting model for Brent crude oil price. Careful attention is paid on finding number of features as input data to achieve best performance for model. Also to overcome unforeseen critical conditions, a crisis index is defined. The results show that with appropriate selection of the training data and crisis index, the model is capable of forecasting oil price in both normal and critical conditions.","PeriodicalId":211301,"journal":{"name":"2010 International Conference on Electronics and Information Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":"{\"title\":\"Monthly Brent oil price forecasting using artificial neural networks and a crisis index\",\"authors\":\"A. Alizadeh, K. Mafinezhad\",\"doi\":\"10.1109/ICEIE.2010.5559818\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The volatility of the oil future price is extremely complex, therefore an accurate forecasting on oil price is an important and challenging topic. This paper presents a GRNN forecasting model for Brent crude oil price. Careful attention is paid on finding number of features as input data to achieve best performance for model. Also to overcome unforeseen critical conditions, a crisis index is defined. The results show that with appropriate selection of the training data and crisis index, the model is capable of forecasting oil price in both normal and critical conditions.\",\"PeriodicalId\":211301,\"journal\":{\"name\":\"2010 International Conference on Electronics and Information Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-09-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"24\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 International Conference on Electronics and Information Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEIE.2010.5559818\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Electronics and Information Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEIE.2010.5559818","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Monthly Brent oil price forecasting using artificial neural networks and a crisis index
The volatility of the oil future price is extremely complex, therefore an accurate forecasting on oil price is an important and challenging topic. This paper presents a GRNN forecasting model for Brent crude oil price. Careful attention is paid on finding number of features as input data to achieve best performance for model. Also to overcome unforeseen critical conditions, a crisis index is defined. The results show that with appropriate selection of the training data and crisis index, the model is capable of forecasting oil price in both normal and critical conditions.