Wavelet Methods

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6 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

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SWIFT: Spatio-temporal Wavelet Integrated Forecasting Framework for Workload Traces
May 7, 2026Zeyuan Ding, Lingfeng Zheng, Dian Ding +1Multivariate Time Series Forecasting Time Series Forecasting
1Shanghai Jiao Tong University, Shanghai, China · 2Shaanxi Normal University, Xi’an, China
An adaptive wavelet-based PINN for problems with localized high-magnitude source
Apr 30, 2026Himanshu Pandey, Ratikanta BeheraPDE Solving Wavelet Methods
Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, 560012, India
Full band denoising of room impulse response in the wavelet domain with dictionary learning
Apr 29, 2026Théophile Dupré, Romain Couderc, Miguel Moleron +3Wavelet Methods Room Acoustics
Trinnov Audio, Neuilly-Plaisance, France
DWTSumm: Discrete Wavelet Transform for Document Summarization
Apr 22, 2026Rana Salama, Abdou Youssef, Mona DiabLegal NLP LLM Grounding
School of Engineering and Applied Science, George Washington University, USA · Language Technologies Institute, Carnegie Mellon University, USA
WaveScat: Wavelet Scattering Front-Ends with Self-Supervised Features for Speech Deepfake Detection
Feb 3, 2026Xi Xuan, Davide Carbone, Wenxin Zhang +1Audio Deepfake Detection Wavelet Methods
Computational Speech Group at the University of Eastern Finland · Laboratoire de Physique de l’Ecole Normale Sup´erieure, Universit´e PSL, CNRS, Sorbonne Universit´e, Universit´e de Paris · School of Computer Science and Technology at the University of Chinese Academy of Sciences and the Department of Mathematics at the University of Toronto

May 7, 2026, cs.DCJ/K move · Enter open · S save

SWIFT: Spatio-temporal Wavelet Integrated Forecasting Framework for Workload Traces

Zeyuan Ding, Lingfeng Zheng, Dian Ding, Guangtao Xue

1Shanghai Jiao Tong University, Shanghai, China · 2Shaanxi Normal University, Xi’an, China

Multivariate Time Series Forecasting Time Series Forecasting Wavelet Methods Spatiotemporal Forecasting

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Accurate cloud workload forecasting is pivotal for efficient resource management but remains challenging as workloads are highly volatile and prone to sudden bursts. Although wavelets preserve temporal locality, rigid fixed bases struggle with complex patterns and isolated processing neglects critical spatial dependencies. To address this, we propose SWIFT, a pure convolutional framework designed for high-efficiency workload forecasting. We introduce a Learnable Cascaded Wavelet Path that reformulates the traditional fixed wavelet bases into adaptive convolutional operators, enabling precise, data-driven feature peeling. Complementing this, our Multivariate Interaction Module sequentially models inter-variable spatial and intra-variable feature interactions to stabilize and refine noisy workload states. Extensive experiments demonstrate that SWIFT achieves SOTA accuracy with linear O(L) complexity, reducing prediction error by up to 31.04% while cutting latency by 79.74%.

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Latest papers 35

SWIFT: Spatio-temporal Wavelet Integrated Forecasting Framework for Workload Traces

An adaptive wavelet-based PINN for problems with localized high-magnitude source

Full band denoising of room impulse response in the wavelet domain with dictionary learning

DWTSumm: Discrete Wavelet Transform for Document Summarization

WaveScat: Wavelet Scattering Front-Ends with Self-Supervised Features for Speech Deepfake Detection