6 papers
Regression Models Meet Foundation Models: A Hybrid-AI Approach to Practical Electricity Price Forecasting
Yunzhong Qiu, Binzhu Li, Hao Wei +5
Electricity market prices exhibit extreme volatility, nonlinearity, and non-stationarity, making accurate forecasting a significant challenge. While cutting-edge time series founda…
Adapt Data to Model: Adaptive Transformation Optimization for Domain-shared Time Series Foundation Models
Yunzhong Qiu, Zhiyao Cen, Zhongyi Pei +2
Large time series models (LTMs) have emerged as powerful tools for universal forecasting, yet they often struggle with the inherent diversity and nonstationarity of real-world time…
DiTS: Multimodal Diffusion Transformers Are Time Series Forecasters
Haoran Zhang, Haixuan Liu, Yong Liu +4
While generative modeling on time series facilitates more capable and flexible probabilistic forecasting, existing generative time series models do not address the multi-dimensiona…
TimesBERT: A BERT-Style Foundation Model for Time Series Understanding
Haoran Zhang, Yong Liu, Yunzhong Qiu +4
Time series analysis is crucial in diverse scenarios. Beyond forecasting, considerable real-world tasks are categorized into classification, imputation, and anomaly detection, unde…
TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables
Yuxuan Wang, Haixu Wu, Jiaxiang Dong +6
Deep models have demonstrated remarkable performance in time series forecasting. However, due to the partially-observed nature of real-world applications, solely focusing on the ta…
TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling
Jiaxiang Dong, Haixu Wu, Yuxuan Wang +4
Time series pre-training has recently garnered wide attention for its potential to reduce labeling expenses and benefit various downstream tasks. Prior methods are mainly based on…