Deep Time Series Models: A Comprehensive Survey and Benchmark

Understand

Time series, characterized by a sequence of data points organized in a discrete-time order, are ubiquitous in real-world scenarios.

• Unlike other data modalities, time series present unique challenges in learning and modeling due to their intricate and dynamic nature, including the entanglement of nonlinear patterns and time-variant trends.
• Recent years have witnessed remarkable breakthroughs in time series analysis, with techniques shifting from traditional statistical methods to contemporary 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.