Large Time Series Forecasting Model in Intelligent Decision-making: A Survey
SHAO Zezhi1 YU Chengqing 1, 2 LI Yujie 1, 2 WANG Fei 1, 2, * XU Yongjun 1, 2
1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; 2. School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 101408, China
Abstract：The heterogeneity of time series data in different scenarios significantly impacts the generalization ability and effectiveness of time series forecasting algorithms in intelligent decision-making, posing a major obstacle to their application. Large time series forecasting models are essential techniques to address this challenge. The latest research trends in the field of time series forecasting are integrated and four im-plementation approaches of large time series forecasting models are explored from a modal perspective from top to bottom: prompt-based methods, fine-tuning-based methods, alignment-based methods, and basic models for time series forecasting. Additionally, the core elements and available technologies in the construction process of large time series forecasting models are sorted out Furthermore, the important challenges and research directions in the future are explored.
邵泽志，余澄庆， 李雨杰， 王飞， 徐勇军. 决策智能中的时间序列预测大模型[J]. 指挥与控制学报, 2025, 11(2): 146-157.
SHAO Zezhi，YU Chengqing, LI Yujie, WANG Fei,XU Yongjun. Large Time Series Forecasting Model in Intelligent Decision-making: A Survey. Journal of Command and Control, 2025, 11(2): 146-157.
2025, Vol. 11 
