Abstract—As a critical branch of time series analysis, time series forecasting (TSF) focuses on predicting future trends based on historical data, and it plays a pivotal role in a wide range of applications, including meteorology, finance, and healthcare. Recently, deep learning has demonstrated significant potential in TSF. While several existing surveys have systematically summarized deep learning-based methods, we complement these foundational works by investigating emerging models, such as large language models (LLMs), and providing an in-depth comparative analysis of distinct models alongside the challenges currently facing the field. Specifically, we propose a hierarchical taxonomy based on model structural dependency, categorizing existing studies into model-specific and model-agnostic frameworks. The model-specific framework is further divided into discriminative and generative paradigms, accompanied by a detailed comparison of these distinct model types. Moreover, we systematically review prevalent timeseries datasets across diverse domains, analyze their key statistics, and summarize evaluation metrics. Finally, we analyze the key challenges currently faced by TSF and explore potential future research directions. Through this systematic review and forward-looking analysis, we aim to provide novel perspectives and establish a clear classification framework of TSF methods.

论文链接：https://ieeexplore.ieee.org/document/11423901.