深度学习中的多步时间序列预测策略:递归策略与密集层多输出策略的结合

Yuxuan Liu
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引用次数: 0

摘要

在实际应用中,在解决时间序列预测问题时,往往需要根据观测数据对未来多个时间点的数据进行预测。这个问题叫做多步时间序列预测。现在有一些解决方案来处理这个问题,但每个解决方案都有自己的优点和不足。因此,本文的目标是将递归策略和多输出策略相结合,提出一种解决多步时间序列预测问题的新方法,以获得比单独使用两者更好的性能。在研究中,作者将使用来自Kaggle的House Hold Energy数据,并进行对比实验,以体现组合策略的优势。实验结果表明,该组合策略优于递归和多输出策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Multi-step Time Series Prediction Strategy in Deep Learning: Combination of Recursive Strategy and Multi-output by Dense Layer Strategy
In practical applications, when solving the problem of time series prediction, it is often necessary to predict the data of multiple time points in the future according to the observed data. It’s a problem called multi-step time series prediction. Now there are some solutions to handle the problem, but each solution has its own advantages and is insufficient. As a result, the goal of this paper is to combine the recursive strategy and the multi-output strategy to propose a new method for solving the multi-step time series prediction problem to gain better performance than either of them alone. In the research, the author will use House Hold Energy data from Kaggle and conduct a contrast experiment to reflect the advantages of the combined strategy. The experiment results show that the combined strategy outperforms the recursive and multi-output strategies.
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