Time series analysis and prediction methods currently excel in quantitative analysis, offering accurate future predictions and diverse statistical indicators, but generally falling short in elucidating the underlying evolution patterns of time series. To gain a more comprehensive understanding and provide insightful explanations, we utilize symbolic regression techniques to derive explicit expressions for the non-linear dynamics in the evolution of time series variables. However, these techniques face challenges in computational efficiency and generalizability across diverse real-world time series data. To overcome these challenges, we propose \textbf{N}eural-\textbf{E}nhanced \textbf{Mo}nte-Carlo \textbf{T}ree \textbf{S}earch (NEMoTS) for time series. NEMoTS leverages the exploration-exploitation balance of Monte-Carlo Tree Search (MCTS), significantly reducing the search space in symbolic regression and improving expression quality. Furthermore, by integrating neural networks with MCTS, NEMoTS not only capitalizes on their superior fitting capabilities to concentrate on more pertinent operations post-search space reduction, but also replaces the complex and time-consuming simulation process, thereby substantially improving computational efficiency and generalizability in time series analysis. NEMoTS offers an efficient and comprehensive approach to time series analysis. Experiments with three real-world datasets demonstrate NEMoTS's significant superiority in performance, efficiency, reliability, and interpretability, making it well-suited for large-scale real-world time series data.

翻译：当前的时间序列分析与预测方法在定量分析方面表现出色，能够提供准确的未来预测和多样化的统计指标，但通常在阐明时间序列的潜在演化模式方面存在不足。为了获得更全面的理解并提供具有洞察力的解释，我们利用符号回归技术来推导时间序列变量演化过程中非线性动力学的显式表达式。然而，这些技术在计算效率以及对多样化现实世界时间序列数据的泛化能力方面面临挑战。为了克服这些挑战，我们提出了一种用于时间序列的**N**eural-**E**nhanced **Mo**nte-Carlo **T**ree **S**earch (NEMoTS) 方法。NEMoTS 利用了蒙特卡洛树搜索（MCTS）的探索-利用平衡，显著减少了符号回归的搜索空间并提高了表达式质量。此外，通过将神经网络与 MCTS 相结合，NEMoTS 不仅能够利用神经网络卓越的拟合能力，在搜索空间缩减后专注于更相关的操作，而且替代了复杂耗时的模拟过程，从而在时间序列分析中大幅提升了计算效率和泛化能力。NEMoTS 为时间序列分析提供了一种高效且全面的方法。在三个真实世界数据集上的实验表明，NEMoTS 在性能、效率、可靠性和可解释性方面均具有显著优势，使其非常适用于大规模的现实世界时间序列数据。