most citedMinusformer: Improving Time Series Forecasting by Progressively Learning Residuals

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

collaborators

6 papers

cs.LG2025

Progressive Supervision via Label Decomposition: An Long-Term and Large-Scale Wireless Traffic Forecasting Method

Daojun Liang, Haixia Zhang, Dongfeng Yuan

Long-term and Large-scale Wireless Traffic Forecasting (LL-WTF) is pivotal for strategic network management and comprehensive planning on a macro scale. However, LL-WTF poses great…

cs.CV2024★ 1 cited

Multi-Head Encoding for Extreme Label Classification

Daojun Liang, Haixia Zhang, Dongfeng Yuan +1

The number of categories of instances in the real world is normally huge, and each instance may contain multiple labels. To distinguish these massive labels utilizing machine learn…

cs.LG2024★ 1 cited

Act Now: A Novel Online Forecasting Framework for Large-Scale Streaming Data

Daojun Liang, Haixia Zhang, Jing Wang +2

In this paper, we find that existing online forecasting methods have the following issues: 1) They do not consider the update frequency of streaming data and directly use labels (f…

cs.LG2024

DistPred: A Distribution-Free Probabilistic Inference Method for Regression and Forecasting

Daojun Liang, Haixia Zhang, Dongfeng Yuan

Traditional regression and prediction tasks often only provide deterministic point estimates. To estimate the distribution or uncertainty of the response variable, traditional meth…

cs.NI2024

Multi-hop Multi-RIS Wireless Communication Systems: Multi-reflection Path Scheduling and Beamforming

Xiaoyan Ma, Haixia Zhang, Xianhao Chen +1

Reconfigurable intelligent surface (RIS) provides a promising way to proactively augment propagation environments for better transmission performance in wireless communications. Ex…

cs.LG2024★ 4 cited

Minusformer: Improving Time Series Forecasting by Progressively Learning Residuals

Daojun Liang, Haixia Zhang, Dongfeng Yuan +2

In this paper, we find that ubiquitous time series (TS) forecasting models are prone to severe overfitting. To cope with this problem, we embrace a de-redundancy approach to progre…