CTS2: Time Series Smoothing with Constrained Reinforcement Learning

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 $\mathbf{C}$onstrained reinforcement learning-based $\mathbf{T}$ime $\mathbf{S}$eries $\mathbf{S}$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.