{"title":"基于多元双向长短期记忆的STL分解股票价格预测","authors":"J. Senoguchi","doi":"10.32996/jcsts.2022.4.2.11","DOIUrl":null,"url":null,"abstract":"With advancements in machine-learning techniques, stock-price movements can ostensibly be forecasted using time-series data. In this study, several different types of long short-term memory (LSTM) are used to predict the closing prices of Japanese stocks five days into the future. Also, in this study, four different features [i.e., simple moving average (SMA), linear weighted moving average (WMA), exponential WMA (EMA), and the Savitzky–Golay (SG) metric] are generated from daily stock-price data and split into two components (i.e., trend and seasonal) by applying seasonal–trend decomposition using Loess (STL) decomposition. The prediction results are evaluated in terms of return, root-mean-square error (RMSE), mean absolute error (MAE), and other relevant measures of accuracy and relevancy. As a result, the multivariate two-way LSTM model yielded the highest overall performance. With respect to the RMSE and MAE of the training data, the multivariate two-way LSTM was not superior to the other models. However, with respect to RMSE and MAE on the validation data, it was the best. Also, the multivariate two-way LSTM model yielded the highest overall performance in terms of the accuracy of the direction of stock prices.","PeriodicalId":417206,"journal":{"name":"Journal of Computer Science and Technology Studies","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Stock Price Prediction through STL Decomposition using Multivariate Two-way Long Short-term Memory\",\"authors\":\"J. Senoguchi\",\"doi\":\"10.32996/jcsts.2022.4.2.11\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With advancements in machine-learning techniques, stock-price movements can ostensibly be forecasted using time-series data. In this study, several different types of long short-term memory (LSTM) are used to predict the closing prices of Japanese stocks five days into the future. Also, in this study, four different features [i.e., simple moving average (SMA), linear weighted moving average (WMA), exponential WMA (EMA), and the Savitzky–Golay (SG) metric] are generated from daily stock-price data and split into two components (i.e., trend and seasonal) by applying seasonal–trend decomposition using Loess (STL) decomposition. The prediction results are evaluated in terms of return, root-mean-square error (RMSE), mean absolute error (MAE), and other relevant measures of accuracy and relevancy. As a result, the multivariate two-way LSTM model yielded the highest overall performance. With respect to the RMSE and MAE of the training data, the multivariate two-way LSTM was not superior to the other models. However, with respect to RMSE and MAE on the validation data, it was the best. Also, the multivariate two-way LSTM model yielded the highest overall performance in terms of the accuracy of the direction of stock prices.\",\"PeriodicalId\":417206,\"journal\":{\"name\":\"Journal of Computer Science and Technology Studies\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Computer Science and Technology Studies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.32996/jcsts.2022.4.2.11\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computer Science and Technology Studies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32996/jcsts.2022.4.2.11","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Stock Price Prediction through STL Decomposition using Multivariate Two-way Long Short-term Memory
With advancements in machine-learning techniques, stock-price movements can ostensibly be forecasted using time-series data. In this study, several different types of long short-term memory (LSTM) are used to predict the closing prices of Japanese stocks five days into the future. Also, in this study, four different features [i.e., simple moving average (SMA), linear weighted moving average (WMA), exponential WMA (EMA), and the Savitzky–Golay (SG) metric] are generated from daily stock-price data and split into two components (i.e., trend and seasonal) by applying seasonal–trend decomposition using Loess (STL) decomposition. The prediction results are evaluated in terms of return, root-mean-square error (RMSE), mean absolute error (MAE), and other relevant measures of accuracy and relevancy. As a result, the multivariate two-way LSTM model yielded the highest overall performance. With respect to the RMSE and MAE of the training data, the multivariate two-way LSTM was not superior to the other models. However, with respect to RMSE and MAE on the validation data, it was the best. Also, the multivariate two-way LSTM model yielded the highest overall performance in terms of the accuracy of the direction of stock prices.