Willian de Assis Pedrobon Ferreira, I. Grout, Alexandre César Rodrigues da Silva
{"title":"利用模糊ARTMAP神经网络预测能量时间序列数据","authors":"Willian de Assis Pedrobon Ferreira, I. Grout, Alexandre César Rodrigues da Silva","doi":"10.1109/ICPEI49860.2020.9431435","DOIUrl":null,"url":null,"abstract":"Time-series forecasting is an important field of machine learning and is fundamental in analyzing trends based on historical data from various sources. In this paper, a fuzzy ARTMAP neural network for time-series forecasting is presented. To validate the proposed system, two energy-related datasets from Great Britain were selected. With a promising processing time and accuracy as good as a traditional machine learning algorithm, the fuzzy ARTMAP neural network has shown that can be a good option to perform forecasting considering different time-based data issues.","PeriodicalId":342582,"journal":{"name":"2020 International Conference on Power, Energy and Innovations (ICPEI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Forecasting energy time-series data using a fuzzy ARTMAP neural network\",\"authors\":\"Willian de Assis Pedrobon Ferreira, I. Grout, Alexandre César Rodrigues da Silva\",\"doi\":\"10.1109/ICPEI49860.2020.9431435\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Time-series forecasting is an important field of machine learning and is fundamental in analyzing trends based on historical data from various sources. In this paper, a fuzzy ARTMAP neural network for time-series forecasting is presented. To validate the proposed system, two energy-related datasets from Great Britain were selected. With a promising processing time and accuracy as good as a traditional machine learning algorithm, the fuzzy ARTMAP neural network has shown that can be a good option to perform forecasting considering different time-based data issues.\",\"PeriodicalId\":342582,\"journal\":{\"name\":\"2020 International Conference on Power, Energy and Innovations (ICPEI)\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Conference on Power, Energy and Innovations (ICPEI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPEI49860.2020.9431435\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Power, Energy and Innovations (ICPEI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPEI49860.2020.9431435","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Forecasting energy time-series data using a fuzzy ARTMAP neural network
Time-series forecasting is an important field of machine learning and is fundamental in analyzing trends based on historical data from various sources. In this paper, a fuzzy ARTMAP neural network for time-series forecasting is presented. To validate the proposed system, two energy-related datasets from Great Britain were selected. With a promising processing time and accuracy as good as a traditional machine learning algorithm, the fuzzy ARTMAP neural network has shown that can be a good option to perform forecasting considering different time-based data issues.