ADTime: Adaptive Multivariate Time Series Forecasting Using LLMs
Large language models (LLMs) have recently demonstrated notable performance, particularly in addressing the challenge of extensive data requirements when training traditional forecasting models. However, these methods encounter significant challenges when applied to high-dimensional and domain-speci...
Saved in:
Main Authors: | Jinglei Pei, Yang Zhang, Ting Liu, Jingbin Yang, Qinghua Wu, Kang Qin |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-04-01
|
Series: | Machine Learning and Knowledge Extraction |
Subjects: | |
Online Access: | https://www.mdpi.com/2504-4990/7/2/35 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Applied time series analysis for managerial forecasting
by: Nelson, Charles R.
Published: (1973) -
Enhanced mixup for improved time series analysis
by: Khoa Tho Anh Nguyen, et al.
Published: (2025-05-01) -
Applied time series analysis proceedings of the international conference held at Houston, Texas, August 1981
Published: (1982) -
A Comparative Evaluation of Time-Series Forecasting Models for Energy Datasets
by: Nikitas Maragkos, et al.
Published: (2025-06-01) -
Resource Time Series Analysis and Forecasting in Large-Scale Virtual Clusters
by: Yue Lin, et al.
Published: (2025-05-01)