{"title":"基于模式股票预测模型的机器学习技术分析","authors":"C. Dadiyala, Asha Ambhaikar","doi":"10.1109/ICSES52305.2021.9633961","DOIUrl":null,"url":null,"abstract":"In Stock Prediction, the aim is to predict future stock values with desirable accuracy. Our research aims to offer a method for technical analysis of pattern based stock prediction using Machine Learning on the historical stock data. The newly designed method is based on GA with the appropriate modifications needed for the prediction. We have performed various experiments using the historical data of a few companies and the results confirmed the accuracy and efficiency of the system as it is generating promising predictions. This designed model executes a prediction process that is not influenced by any other external factors.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"69 1","pages":"1-9"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Technical Analysis of Pattern Based Stock Prediction Model Using Machine Learning\",\"authors\":\"C. Dadiyala, Asha Ambhaikar\",\"doi\":\"10.1109/ICSES52305.2021.9633961\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In Stock Prediction, the aim is to predict future stock values with desirable accuracy. Our research aims to offer a method for technical analysis of pattern based stock prediction using Machine Learning on the historical stock data. The newly designed method is based on GA with the appropriate modifications needed for the prediction. We have performed various experiments using the historical data of a few companies and the results confirmed the accuracy and efficiency of the system as it is generating promising predictions. This designed model executes a prediction process that is not influenced by any other external factors.\",\"PeriodicalId\":6777,\"journal\":{\"name\":\"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)\",\"volume\":\"69 1\",\"pages\":\"1-9\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSES52305.2021.9633961\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSES52305.2021.9633961","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Technical Analysis of Pattern Based Stock Prediction Model Using Machine Learning
In Stock Prediction, the aim is to predict future stock values with desirable accuracy. Our research aims to offer a method for technical analysis of pattern based stock prediction using Machine Learning on the historical stock data. The newly designed method is based on GA with the appropriate modifications needed for the prediction. We have performed various experiments using the historical data of a few companies and the results confirmed the accuracy and efficiency of the system as it is generating promising predictions. This designed model executes a prediction process that is not influenced by any other external factors.