Time series analysis is a fundamental component of machine learning, especially in astrophysics and cosmology where temporal data abound. This chapter provides a pedagogical review of time series analysis techniques from a machine learning perspective. We cover the basic concepts of time series (stationarity, autocorrelation, seasonality), classical statistical models (autoregressive, moving average, ARIMA, exponential smoothing, state-space models), and modern machine learning approaches. In particular, we discuss how traditional statistical methods lay the groundwork, and then explore machine learning methods for time series, including feature-based regression, tree-based ensemble methods, hidden Markov models, Gaussian processes, and deep learning models (recurrent neural networks, convolutional networks, transformers). Throughout, we illustrate with examples drawn from multiple domains (e.g. astronomy, weather forecasting, finance) to emphasize common principles. The goal is to equip readers with both the theoretical understanding and practical context to apply machine learning techniques for time series analysis in their research.

翻译：时间序列分析是机器学习的基础组成部分，尤其在时间数据丰富的天体物理学和宇宙学领域至关重要。本章从机器学习视角对时间序列分析技术进行教学性综述。我们涵盖时间序列的基本概念（平稳性、自相关性、季节性）、经典统计模型（自回归、滑动平均、ARIMA、指数平滑、状态空间模型）及现代机器学习方法。特别地，我们讨论传统统计方法如何奠定基础，继而探索时间序列的机器学习方法，包括基于特征的回归、基于树的集成方法、隐马尔可夫模型、高斯过程以及深度学习模型（循环神经网络、卷积网络、Transformer）。全章通过多领域实例（如天文学、天气预报、金融）阐明共同原理。旨在让读者既掌握理论理解又获得实践背景，从而在各自研究中应用机器学习技术进行时间序列分析。