arXiv:2609.15148v1 Announce Type: new
Abstract: Transfer learning is an effective technique for addressing data scarcity in deep learning for time series classification, but its success depends on th...
By Jiseok Lee, Brian Kenji Iwana
WaveletDiff is a diffusion-based framework that generates time series by training directly on wavelet coefficients, leveraging multi-resolution structure through level‑specific transformers and cross‑level attention with adaptive gating. The model incorporates Parseval‑theorem‑based energy constraints to preserve time‑frequency properties during diffusion. Experiments on six real‑world datasets from energy, finance, and neuroscience show that WaveletDiff outperforms several diffusion baselines and competes with the VAE/transformer‑based MSDformer, achieving lower discriminative and Context‑FID scores while using fewer parameters and less training time.
By Yu-Hsiang Wang, Olgica Milenkovic
The paper introduces a spectrally-aligned latent-flow model for time‑series generation that trains the latent space to preserve dynamical properties relevant to synthetic data quality. By incorporating fine‑tuning losses based on Fourier, wavelet, and signature transforms, the method mitigates spectral mismatches caused by latent compression and ensures alignment with true signals in terms of smoothness and targeted spectral content. Experiments on real‑world long‑range univariate and multivariate benchmarks show that the aligned model outperforms a base latent‑flow model and state‑of‑the‑art approaches in signal realism, computational efficiency, and local structure alignment.
By Camilo Carvajal Reyes, Felipe Tobar
arXiv:2606. 26487v1 Announce Type: cross Abstract: Large language models (LLMs) are attractive for context-aware time series forecasting because they can integrate heterogeneous textual signals, yet their discrete, language-oriented tokenization and embedding interfaces are misaligned with continuous numerical values, often harming numerical ordering and forecasting reliability.
By Defu Cao, Zijie Lei, Muyan Weng, Jiao Sun, Yan Liu
The paper introduces m-WCN, an end‑to‑end deep learning framework that neuralizes multi‑wavelet decomposition to jointly extract temporal patterns and frequency components from time series. Two task‑specific architectures built on m‑WCN—TFBC for classification and FTB for forecasting—are shown to outperform baseline models on 64 UCR datasets and seven forecasting benchmarks, achieving average improvements of nearly 20% in both tasks. The approach leverages trainable convolutional operators and orthogonality constraints to produce interpretable multi‑resolution representations.
By Xiaohan Jiang, Jingyuan Wang, Jiahao Ji, Yongyao Wang, Chen Yang, Junjie Wu
arXiv:2606. 18729v1 Announce Type: cross Abstract: Data valuation quantifies the intrinsic quality of individual samples to enable principled data curation, quality control, and robust learning.
By Wenqin Liu, Weizhi Quan, Aoqi Zuo, Erdun Gao, Vu Nguyen, Dino Sejdinovic, Howard Bondell, Mingming Gong