Time series classification is a pivotal task in machine learning and data mining and has garnered widespread attention in recent years. With the advent of the significant data era, the application of time series data in finance, healthcare, transportation, energy, and other fields has been increasing, making the research and application of time series classification increasingly significant. This paper summarizes and organizes the relevant research methods from the perspective of traditional time series classification methods and time series classification methods based on deep learning. Traditional methods encompass distance-based, feature-based, interval-based, and dictionary-based approaches. Deep learning-based methods, on the other hand, include convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory networks (LSTM), gated recurrent units (GRU), and Transformer models. Furthermore, this paper also introduces the characteristics of time series data and common indicators for evaluating time series classification performance and classifies and organizes public time series datasets.