most citedBLAST: Balanced Sampling Time Series Corpus for Universal Forecasting Models

3 citations · 3 across the 6 of their papers we have counts for

collaborators

7 papers

cs.AI2025

KnowCoder-A1: Incentivizing Agentic Reasoning Capability with Outcome Supervision for KBQA

Zhuo Chen, Fei Wang, Zixuan Li +6

Knowledge Base Question Answering (KBQA) aims to answer natural-language questions over a structured Knowledge Base (KB). Recent work improves KBQA by adopting an agentic reasoning…

cs.LG2025

APT: Affine Prototype-Timestamp For Time Series Forecasting Under Distribution Shift

Yujie Li, Zezhi Shao, Chengqing Yu +4

Time series forecasting under distribution shift remains challenging, as existing deep learning models often rely on local statistical normalization (e.g., mean and variance) that…

cs.LG2025

Selective Learning for Deep Time Series Forecasting

Yisong Fu, Zezhi Shao, Chengqing Yu +5

Benefiting from high capacity for capturing complex temporal patterns, deep learning (DL) has significantly advanced time series forecasting (TSF). However, deep models tend to suf…

cs.LG2025

ARIES: Relation Assessment and Model Recommendation for Deep Time Series Forecasting

Fei Wang, Yujie Li, Zezhi Shao +5

Recent advancements in deep learning models for time series forecasting have been significant. These models often leverage fundamental time series properties such as seasonality an…

cs.LG2025

Merlin: Multi-View Representation Learning for Robust Multivariate Time Series Forecasting with Unfixed Missing Rates

Chengqing Yu, Fei Wang, Chuanguang Yang +6

Multivariate Time Series Forecasting (MTSF) involves predicting future values of multiple interrelated time series. Recently, deep learning-based MTSF models have gained significan…

cs.LG20253 cited

BLAST: Balanced Sampling Time Series Corpus for Universal Forecasting Models

Zezhi Shao, Yujie Li, Fei Wang +7

The advent of universal time series forecasting models has revolutionized zero-shot forecasting across diverse domains, yet the critical role of data diversity in training these mo…