17 citations · 31 across the 14 of their papers we have counts for
7 papers · 1 filter
Fidel-TS: A High-Fidelity Multimodal Benchmark for Time Series Forecasting
Zhijian Xu, Wanxu Cai, Xilin Dai +2
The evaluation of time series forecasting models is hindered by a lack of high-quality benchmarks, leading to overestimated assessments of progress. Existing datasets suffer from i…
From Samples to Scenarios: A New Paradigm for Probabilistic Forecasting
Xilin Dai, Zhijian Xu, Wanxu Cai +1
Most state-of-the-art probabilistic time series forecasting models rely on sampling to represent future uncertainty. However, this paradigm suffers from inherent limitations, such…
Intervention-Aware Forecasting: Breaking Historical Limits from a System Perspective
Zhijian Xu, Hao Wang, Qiang Xu
Traditional time series forecasting methods predominantly rely on historical data patterns, neglecting external interventions that significantly shape future dynamics. Through cont…
Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning
Yuxuan Bian, Xuan Ju, Jiangtong Li +3
In this study, we present aLLM4TS, an innovative framework that adapts Large Language Models (LLMs) for time-series representation learning. Central to our approach is that we reco…
FITS: Modeling Time Series with Parameters
Zhijian Xu, Ailing Zeng, Qiang Xu
In this paper, we introduce FITS, a lightweight yet powerful model for time series analysis. Unlike existing models that directly process raw time-domain data, FITS operates on the…
FrAug: Frequency Domain Augmentation for Time Series Forecasting
Muxi Chen, Zhijian Xu, Ailing Zeng +1
Data augmentation (DA) has become a de facto solution to expand training data size for deep learning. With the proliferation of deep models for time series analysis, various time s…