{"title":"神经网络(MLP、RBFNN、ERNN、JRNN)模型在巴勒斯坦银行股价预测中的性能比较","authors":"Shady I. Altelbany, Anwar A. Abualhussein","doi":"10.52113/6/2021-11/8-28","DOIUrl":null,"url":null,"abstract":"This study aimed to Performance Comparison of Neural Networks (MLP, RBFNN, ERNN, JRNN) Models for the time series data of a monthly Stock Prices to Bank of Palestine from Nov. 2005 to Oct. 2020, and comparing between models to see which one is better in forecasting. The results of applying the methods were compared through the (MAPE, MAE, RMSE), the most accurate model is ERNN 14-25-1 with minimum forecast measure error.","PeriodicalId":426963,"journal":{"name":"Muthanna Journal of Administrative and Economic Sciences","volume":"58 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Performance Comparison of Neural Networks (MLP, RBFNN, ERNN, JRNN) Models for Stock Prices Forecasting to Bank of Palestine\",\"authors\":\"Shady I. Altelbany, Anwar A. Abualhussein\",\"doi\":\"10.52113/6/2021-11/8-28\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study aimed to Performance Comparison of Neural Networks (MLP, RBFNN, ERNN, JRNN) Models for the time series data of a monthly Stock Prices to Bank of Palestine from Nov. 2005 to Oct. 2020, and comparing between models to see which one is better in forecasting. The results of applying the methods were compared through the (MAPE, MAE, RMSE), the most accurate model is ERNN 14-25-1 with minimum forecast measure error.\",\"PeriodicalId\":426963,\"journal\":{\"name\":\"Muthanna Journal of Administrative and Economic Sciences\",\"volume\":\"58 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Muthanna Journal of Administrative and Economic Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.52113/6/2021-11/8-28\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Muthanna Journal of Administrative and Economic Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.52113/6/2021-11/8-28","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Performance Comparison of Neural Networks (MLP, RBFNN, ERNN, JRNN) Models for Stock Prices Forecasting to Bank of Palestine
This study aimed to Performance Comparison of Neural Networks (MLP, RBFNN, ERNN, JRNN) Models for the time series data of a monthly Stock Prices to Bank of Palestine from Nov. 2005 to Oct. 2020, and comparing between models to see which one is better in forecasting. The results of applying the methods were compared through the (MAPE, MAE, RMSE), the most accurate model is ERNN 14-25-1 with minimum forecast measure error.
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