CTS2: Time Series Smoothing with Constrained Reinforcement Learning
CTS2: Time Series Smoothing with Constrained Reinforcement Learning
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发表时间：
2021
影响因子：
5.2
通讯作者：
Yongshuai Liu;Xin Liu;I. Tsang;X. Liu;Liu Liu-Liu
中科院分区：
文献类型：
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作者：
Yongshuai Liu;Xin Liu;I. Tsang;X. Liu;Liu Liu-Liu
时间序列平滑是时间序列分析和预测的基础。它有助于识别时间序列的趋势和模式。然而，不规则扰动的存在扰乱了时间序列的平稳性，扭曲了信息。时间序列平滑的目标是在保留尽可能多的信息的同时消除这些扰动。现有的平滑算法可以完全自由地对数据点进行修正，这往往会对时间序列进行过度平滑并丢失信息。据我们所知，他们中没有人考虑限制数据修正。此外，现有的大多数方法要么不能实时平滑，要么需要在不同的场景下手动调整参数。为了在考虑数据校正约束的同时提高平滑性能，我们提出了一种基于约束强化学习的时间序列S序列S平滑方法，简称CTS2。具体地说，我们首先将光滑问题描述为约束马尔可夫决策过程(fifiDecision Process)。然后，我们结合数据校正约束来限制每个点的校正量。最后，我们学习了一个具有线性投影层的政策网络来平滑时间序列。线性投影层确保所有数据校正满足数据校正约束。我们在合成和真实时间序列数据集上对CTS-2进行了评估，结果表明，CTS-2成功地实时平滑了时间序列，满足了fi的所有校正约束，并且在各种场景下都能很好地工作ffi。
Time series smoothing is essential for time series analysis and forecasting. It helps to identify trends and patterns of time series. However, the presence of irregular perturbations disrupt the time series smoothness and distort information. The goal of time series smoothing is to remove these perturbations while preserving as much information as possible. Existing smoothing algorithms have complete freedom to make corrections to the data points which often over smooth the time series and lose information. None of them considers constraining data corrections to the best of our knowledge. Moreover, most existing methods either do not smooth in real-time or their parameters need to be hand-tuned in different scenarios. To improve smoothing performance while considering data correction constraints, we propose a C onstrained reinforcement learning-based T ime S eries S moothing method, or CTS 2 . Specifically, we first formulate the smoothing problem as a Constrained Markov Decision Process (CMDP). We then incorporate data correction constraints to restrict the amount of correction at each point. Finally, we learn a policy network with a linear projection layer to smooth the time series. The linear projection layer ensures that all data corrections satisfy the data correction constraints. We evaluate CTS 2 on both synthetic and real-world time series datasets; our results show that CTS 2 successfully smooths time series in real-time, satisfies all the correction constraints, and works efficiently in a variety of scenarios.