6 papers · 1 filter
PrismFlow: Residual Dynamics for Flow Matching in Time-Series Generation
Junru Zhang, Lang Feng, Jinbo Wang +6
Generating high-quality time-series data is challenging because real-world signals often exhibit multimodal patterns and multiscale dynamics, including oscillations and high-freque…
A Unified Shape-Aware Foundation Model for Time Series Classification
Zhen Liu, Yucheng Wang, Boyuan Li +4
Foundation models pre-trained on large-scale source datasets are reshaping the traditional training paradigm for time series classification. However, existing time series foundatio…
Learning Soft Sparse Shapes for Efficient Time-Series Classification
Zhen Liu, Yicheng Luo, Boyuan Li +3
Shapelets are discriminative subsequences (or shapes) with high interpretability in time series classification. Due to the time-intensive nature of shapelet discovery, existing sha…
Bridging Distribution Gaps in Time Series Foundation Model Pretraining with Prototype-Guided Normalization
Peiliang Gong, Emadeldeen Eldele, Min Wu +3
Foundation models have achieved remarkable success across diverse machine-learning domains through large-scale pretraining on large, diverse datasets. However, pretraining on such…
Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution Shift
Yanru Sun, Zongxia Xie, Emadeldeen Eldele +3
Time series forecasting, which aims to predict future values based on historical data, has garnered significant attention due to its broad range of applications. However, real-worl…
Explaining Time Series via Contrastive and Locally Sparse Perturbations
Zichuan Liu, Yingying Zhang, Tianchun Wang +8
Explaining multivariate time series is a compound challenge, as it requires identifying important locations in the time series and matching complex temporal patterns. Although prev…