基于多元双向长短期记忆的STL分解股票价格预测

J. Senoguchi
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引用次数: 1

摘要

随着机器学习技术的进步,股价走势表面上可以用时间序列数据来预测。在本研究中,使用几种不同类型的长短期记忆(LSTM)来预测未来五天的日本股票收盘价格。此外,本研究还利用日股价数据生成了简单移动平均线(SMA)、线性加权移动平均线(WMA)、指数移动平均线(EMA)和Savitzky-Golay (SG)指标四种不同的特征,并利用黄土(STL)分解进行季节趋势分解,将其分为趋势和季节两部分。预测结果是根据回报、均方根误差(RMSE)、平均绝对误差(MAE)和其他相关的准确性和相关性来评估的。因此,多元双向LSTM模型产生了最高的整体性能。对于训练数据的RMSE和MAE,多元双向LSTM并不优于其他模型。然而,相对于验证数据的RMSE和MAE,它是最好的。此外,多元双向LSTM模型在股票价格方向的准确性方面产生了最高的整体表现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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