  Time series, characterized by a sequence of data points arranged in a
discrete-time order, are ubiquitous in real-world applications. Different from
other modalities, time series present unique challenges due to their complex
and dynamic nature, including the entanglement of nonlinear patterns and
time-variant trends. Analyzing time series data is of great significance in
real-world scenarios and has been widely studied over centuries. Recent years
have witnessed remarkable breakthroughs in the time series community, with
techniques shifting from traditional statistical methods to advanced deep
learning models. In this paper, we delve into the design of deep time series
models across various analysis tasks and review the existing literature from
two perspectives: basic modules and model architectures. Further, we develop
and release Time Series Library (TSLib) as a fair benchmark of deep time series
models for diverse analysis tasks, which implements 24 mainstream models,
covers 30 datasets from different domains, and supports five prevalent analysis
tasks. Based on TSLib, we thoroughly evaluate 12 advanced deep time series
models on different tasks. Empirical results indicate that models with specific
structures are well-suited for distinct analytical tasks, which offers insights
for research and adoption of deep time series models. Code is available at
https://github.com/thuml/Time-Series-Library.
