Multi-step Ahead Time Series Forecasting for Different Data Patterns Based on LSTM Recurrent Neural Network

L. Yunpeng, Hou Di, Bao Junpeng, Qi Yong
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引用次数: 9

Abstract

Time series prediction problems can play an important role in many areas, and multi-step ahead time series forecast, like river flow forecast, stock price forecast, could help people to make right decisions. Many predictive models do not work very well in multi-step ahead predictions. LSTM (Long Short-Term Memory) is an iterative structure in the hidden layer of the recurrent neural network which could capture the long-term dependency in time series. In this paper, we try to model different types of data patterns, use LSTM RNN for multi-step ahead prediction, and compare the prediction result with other traditional models.
基于LSTM递归神经网络的不同数据模式多步提前时间序列预测
时间序列预测问题可以在许多领域发挥重要作用,多步超前时间序列预测,如河流流量预测、股票价格预测,可以帮助人们做出正确的决策。许多预测模型在多步预测中并不能很好地工作。LSTM (Long - Short-Term Memory)是递归神经网络隐层中的一种迭代结构,能够捕捉时间序列中的长期依赖关系。在本文中,我们尝试对不同类型的数据模式进行建模,使用LSTM RNN进行多步预测,并将预测结果与其他传统模型进行比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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