Multi-source Transfer Learning of Time Series with a Shapelet-based Distance Measure

• URL: http://arxiv.org/abs/2609.15148v1
• Date: Mon, 14 Sep 2026 07:23:19 GMT
• Title: Multi-source Transfer Learning of Time Series with a Shapelet-based Distance Measure
• Abstract summary: This paper introduces a training-free source selection method named Shapelet Matching.<n>Shapelet Matching quantifies dataset similarity by comparing the extracted sets of shapelets.<n>We select several source datasets based on their shapelet-based similarity scores, combine them into a single multi-source dataset, and use this aggregated dataset for pre-training.
• Score: 3.7814216736076425
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Transfer learning is an effective technique for addressing data scarcity in deep learning for time series classification, but its success depends on the selection of source datasets. Conventional transferability estimation methods are often computationally expensive, as they require fully pre-training a model on each potential source dataset to assess its suitability. This paper introduces a novel, training-free source selection method named Shapelet Matching. Our approach first identifies discriminative shapelets from the target and potential source datasets. Then, Shapelet Matching quantifies dataset similarity by comparing the extracted sets of shapelets. To mitigate the risk of negative transfer from selecting an unsuitable single source, we introduce a multi-source transfer learning method. We select several source datasets based on their shapelet-based similarity scores, combine them into a single multi-source dataset, and use this aggregated dataset for pre-training. The model is then fine-tuned on the target task. We evaluated our method on 128 datasets from the UCR Archive using both temporal CNN and Transformer architectures. The empirical results demonstrate that our multi-source pre-training reduces the risk of negative transfer on average. Shapelet Matching achieves the strongest performance for the CNN backbone and remains competitive for patch-based Transformer architectures, while avoiding the cost of pre-training a separate model for every candidate source.

Related papers

• MIRA: Mid-training Rubric Anchoring for Source-Aware Data Selection [59.34281845159177]
  We propose MIRA, a source-aware filtering framework based on self-anchored rubric discovery.<n>On code-oriented mid-training with 21 sources and 5 source groups, MIRA outperforms selection baselines across nine code benchmarks.
  arXiv  Detail & Related papers  (2026-05-28T17:40:40Z)
• Learning Multi-Indicator Weights for Data Selection: A Joint Task-Model Adaptation Framework with Efficient Proxies [50.39041754816285]
  We propose a framework for learning multi-validation weights that jointly adapts data selection to both the downstream task and the specific model.<n>Our approach achieves performance comparable to or exceeding full-dataset tuning while using only 30% of the training samples on GSM8K.
  arXiv  Detail & Related papers  (2026-05-10T17:30:16Z)
• Federated Online Learning for Heterogeneous Multisource Streaming Data [0.0]
  Federated learning has emerged as an essential paradigm for distributed multi-source data analysis under privacy concerns.<n>In this paper, we propose a federated online learning (FOL) method for distributed multi-source streaming data analysis.
  arXiv  Detail & Related papers  (2025-08-08T19:08:53Z)
• SPaRFT: Self-Paced Reinforcement Fine-Tuning for Large Language Models [51.74498855100541]
  Large language models (LLMs) have shown strong reasoning capabilities when fine-tuned with reinforcement learning (RL)<n>We propose textbfSPaRFT, a self-paced learning framework that enables efficient learning based on the capability of the model being trained.
  arXiv  Detail & Related papers  (2025-08-07T03:50:48Z)
• Swift Cross-Dataset Pruning: Enhancing Fine-Tuning Efficiency in Natural Language Understanding [2.379669478864599]
  Current cross-dataset pruning techniques for fine-tuning often rely on computationally expensive sample ranking processes.<n>We propose Swift Cross-Dataset Pruning (SCDP), which uses TF-IDF embeddings with geometric median to rapidly evaluate sample importance.<n> Experimental results on six diverse datasets demonstrate the effectiveness of our method, spanning various tasks and scales.
  arXiv  Detail & Related papers  (2025-01-05T03:52:04Z)
• Adapt-$\infty$: Scalable Continual Multimodal Instruction Tuning via Dynamic Data Selection [89.42023974249122]
  Adapt-$infty$ is a new multi-way and adaptive data selection approach for lifelong instruction tuning.<n>We construct pseudo-skill clusters by grouping gradient-based sample vectors.<n>We select the best-performing data selector for each skill cluster from a pool of selector experts.<n>This data selector samples a subset of the most important samples from each skill cluster for training.
  arXiv  Detail & Related papers  (2024-10-14T15:48:09Z)
• Model Selection with a Shapelet-based Distance Measure for Multi-source Transfer Learning in Time Series Classification [6.875312133832079]
  We propose a novel method of selecting and using multiple datasets for transfer learning for time series classification. Our method combines multiple datasets as one source dataset for pre-training neural networks. We demonstrate that it is possible to increase the performance of temporal convolutional neural networks (CNN) on time series datasets.
  arXiv  Detail & Related papers  (2024-09-30T06:57:30Z)
• Diffusion-Based Neural Network Weights Generation [80.89706112736353]
  D2NWG is a diffusion-based neural network weights generation technique that efficiently produces high-performing weights for transfer learning. Our method extends generative hyper-representation learning to recast the latent diffusion paradigm for neural network weights generation. Our approach is scalable to large architectures such as large language models (LLMs), overcoming the limitations of current parameter generation techniques.
  arXiv  Detail & Related papers  (2024-02-28T08:34:23Z)
• Exploring Data Redundancy in Real-world Image Classification through Data Selection [20.389636181891515]
  Deep learning models often require large amounts of data for training, leading to increased costs. We present two data valuation metrics based on Synaptic Intelligence and gradient norms, respectively, to study redundancy in real-world image data. Online and offline data selection algorithms are then proposed via clustering and grouping based on the examined data values.
  arXiv  Detail & Related papers  (2023-06-25T03:31:05Z)
• Ensemble Transfer Learning for Multilingual Coreference Resolution [60.409789753164944]
  A problem that frequently occurs when working with a non-English language is the scarcity of annotated training data. We design a simple but effective ensemble-based framework that combines various transfer learning techniques. We also propose a low-cost TL method that bootstraps coreference resolution models by utilizing Wikipedia anchor texts.
  arXiv  Detail & Related papers  (2023-01-22T18:22:55Z)
• Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptation [63.24594955429465]
  Multi-source entity linkage is critical in high-impact applications such as data cleaning and user stitching. AdaMEL is a deep transfer learning framework that learns generic high-level knowledge to perform multi-source entity linkage. Our framework achieves state-of-the-art results with 8.21% improvement on average over methods based on supervised learning.
  arXiv  Detail & Related papers  (2021-10-27T15:20:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.