arXiv Machine Learning
Multi-source Transfer Learning of Time Series with a Shapelet-based Distance Measure
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Wavelet Flow Matching for Time Series
The paper introduces Wavelet Flow Matching, a method for generating multivariate time series by applying flow matching to multilevel discrete wavelet coefficients. By working in the wavelet domain, the model captures coarse-to-fine temporal structure implicitly and uses a channel-token transformer to model cross-channel dependencies. Experiments on seven benchmark datasets and four sequence lengths show that the approach matches or surpasses existing methods, especially in Context-FID and discriminative score metrics.
arXiv Machine Learning
Aug 11
CPDA: Class-Conditional Path Distribution Alignment for Unsupervised Time-Series Domain Adaptation
arXiv:2608. 09193v1 Announce Type: cross Abstract: Unsupervised time-series domain adaptation (DA) addresses the challenge of transferring a classifier from a labeled source domain to an unlabeled target domain under distribution shifts induced by different users, sensors, devices, acquisition conditions, or temporal dynamics.
arXiv Machine Learning
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Unlocking Pretrained Vision Transformers for Time Series Classification
arXiv:2506.08641v3 Announce Type: replace Abstract: Adapting vision models for time series analysis is compelling, yet all existing approaches are falling short of dedicated time series foundation mo...
arXiv AI
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Neuralized Multi-Wavelet Decomposition for Time Series Classification and Forecasting
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.
arXiv AI
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TLDChoiceNet: Quantitatively Choosing a Transfer Learning Dataset
arXiv:2608. 09091v1 Announce Type: cross Abstract: Transfer learning is particularly useful in settings with limited training data, and within image classification it is common to transfer learn upon massive datasets like ImageNet , CIFAR-100, or COCO .
arXiv AI
1d ago
WinoTS: Wavelet-based Self-Distillation for Time Series Models
WinoTS introduces a wavelet‑based self‑distillation framework for time‑series models that uses time‑frequency augmentations to create multi‑scale structural views, avoiding distortion of signal dynamics. The method outperforms state‑of‑the‑art baselines in long‑term forecasting, cross‑domain zero‑shot transfer, and unsupervised anomaly detection, and linear probing on frozen representations often beats fully supervised training from scratch. Ablation studies show WinoTS is architecture‑agnostic and demonstrates that time‑frequency transformations offer a principled alternative to vision‑style spatial augmentations.