{"title":"电力批发市场ARFIMA模型的预测效率","authors":"Y. Balagula","doi":"10.52897/978-5-7310-5861-2-2022-16-1-4-9","DOIUrl":null,"url":null,"abstract":"The comparative predictive efficiency of the ARFIMA model for time series of hourly wholesale electricity prices in the markets of a number of countries and regions is investigated. Various aspects of the model are analyzed. It is shown that taking into account long memory in the model increases the accuracy of the forecast.","PeriodicalId":436799,"journal":{"name":"Collection of scientific articles","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predictive efficiency of the ARFIMA model for wholesale electricity markets\",\"authors\":\"Y. Balagula\",\"doi\":\"10.52897/978-5-7310-5861-2-2022-16-1-4-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The comparative predictive efficiency of the ARFIMA model for time series of hourly wholesale electricity prices in the markets of a number of countries and regions is investigated. Various aspects of the model are analyzed. It is shown that taking into account long memory in the model increases the accuracy of the forecast.\",\"PeriodicalId\":436799,\"journal\":{\"name\":\"Collection of scientific articles\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Collection of scientific articles\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.52897/978-5-7310-5861-2-2022-16-1-4-9\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Collection of scientific articles","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.52897/978-5-7310-5861-2-2022-16-1-4-9","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Predictive efficiency of the ARFIMA model for wholesale electricity markets
The comparative predictive efficiency of the ARFIMA model for time series of hourly wholesale electricity prices in the markets of a number of countries and regions is investigated. Various aspects of the model are analyzed. It is shown that taking into account long memory in the model increases the accuracy of the forecast.
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