activity
20242026
most citedGenerative Pretrained Hierarchical Transformer for Time Series Forecasting

37 citations · 53 across the 4 of their papers we have counts for

collaborators

7 papers

cs.LG2026

World2TS: Real-World Event Context for Time-Series Forecasting

Zhiding Liu, Jiqian Yang, Mingyue Cheng +2

Time-series forecasting plays a vital role in a wide range of real-world applications. Recent approaches have achieved strong performance by modeling temporal correlations and cros…

cs.LG2025

Improving Time Series Forecasting via Instance-aware Post-hoc Revision

Zhiding Liu, Mingyue Cheng, Guanhao Zhao +3

Time series forecasting plays a vital role in various real-world applications and has attracted significant attention in recent decades. While recent methods have achieved remarkab…

cs.LG2024

DisenTS: Disentangled Channel Evolving Pattern Modeling for Multivariate Time Series Forecasting

Zhiding Liu, Jiqian Yang, Qingyang Mao +5

Multivariate time series forecasting plays a crucial role in various real-world applications. Significant efforts have been made to integrate advanced network architectures and tra…

cs.CL2024★ 3 cited

Multi-Source Knowledge Pruning for Retrieval-Augmented Generation: A Benchmark and Empirical Study

Shuo Yu, Mingyue Cheng, Qi Liu +6

Retrieval-augmented generation (RAG) is increasingly recognized as an effective approach to mitigating the hallucination of large language models (LLMs) through the integration of…

cs.CL2024★ 8 cited

Towards Personalized Evaluation of Large Language Models with An Anonymous Crowd-Sourcing Platform

Mingyue Cheng, Hao Zhang, Jiqian Yang +7

Large language model evaluation plays a pivotal role in the enhancement of its capacity. Previously, numerous methods for evaluating large language models have been proposed in thi…

cs.LG2024★ 5 cited

ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis

Mingyue Cheng, Jiqian Yang, Tingyue Pan +2

Designing effective models for learning time series representations is foundational for time series analysis. Many previous works have explored time series representation modeling…

cs.LG2024★ 37 cited

Generative Pretrained Hierarchical Transformer for Time Series Forecasting

Zhiding Liu, Jiqian Yang, Mingyue Cheng +2

Recent efforts have been dedicated to enhancing time series forecasting accuracy by introducing advanced network architectures and self-supervised pretraining strategies. Neverthel…