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20242026
most citedPathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting

37 citations · 40 across the 7 of their papers we have counts for

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6 papers · 1 filter

cs.LG2025

CC-Time: Cross-Model and Cross-Modality Time Series Forecasting

Peng Chen, Yihang Wang, Yang Shu +6

With the success of pre-trained language models (PLMs) in various application fields beyond natural language processing, language models have raised emerging attention in the field…

cs.LG2025

LightGTS: A Lightweight General Time Series Forecasting Model

Yihang Wang, Yuying Qiu, Peng Chen +5

Existing works on general time series forecasting build foundation models with heavy model parameters through large-scale multi-source pre-training. These models achieve superior g…

cs.LG2024★ 1 cited

CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching

Xingjian Wu, Xiangfei Qiu, Zhengyu Li +5

Anomaly detection in multivariate time series is challenging as heterogeneous subsequence anomalies may occur. Reconstruction-based methods, which focus on learning normal patterns…

cs.LG2024★ 2 cited

TSFM-Bench: A Comprehensive and Unified Benchmark of Foundation Models for Time Series Forecasting

Zhe Li, Xiangfei Qiu, Peng Chen +8

Time Series Forecasting (TSF) is key functionality in numerous fields, such as financial investment, weather services, and energy management. Although increasingly capable TSF meth…

cs.LG2024

Towards a General Time Series Forecasting Model with Unified Representation and Adaptive Transfer

Yihang Wang, Yuying Qiu, Peng Chen +6

With the growing availability of multi-domain time series data, there is an increasing demand for general forecasting models pre-trained on multi-source datasets to support diverse…

cs.LG2024★ 37 cited

Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting

Peng Chen, Yingying Zhang, Yunyao Cheng +5

Transformers for time series forecasting mainly model time series from limited or fixed scales, making it challenging to capture different characteristics spanning various scales.…