# Interpretable Time-series Classification on Few-shot Samples

arXiv: https://arxiv.org/abs/2006.02031 · HTML: https://mlsanity.com/paper/2006.02031/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2020.

## Built on by (most cited first, 12 of 12)

- [Few-Shot Forecasting of Time-Series with Heterogeneous Channels](https://mlsanity.com/paper/2204.03456/) (2022)
- [A Survey of Few-Shot Learning for Biomedical Time Series](https://mlsanity.com/paper/2405.02485/) (2024)
- [Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification](https://mlsanity.com/paper/2410.15681/) (2024)
- [Multi-Stage Prototype Learning for Interpretable Time Series Classification](https://mlsanity.com/paper/2106.09636/) (2021)
- [Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies](https://mlsanity.com/paper/2503.23775/) (2025)
- [Incorporating Expert Rules into Neural Networks in the Framework of Concept-Based Learning](https://mlsanity.com/paper/2402.14726/) (2024)
- [Large Language Models are Few-shot Multivariate Time Series Classifiers](https://mlsanity.com/paper/2502.00059/) (2025)
- [FI-CBL: A Probabilistic Method for Concept-Based Learning with Expert Rules](https://mlsanity.com/paper/2406.19897/) (2024)
- [ProtoTSNet: Interpretable Multivariate Time Series Classification With Prototypical Parts](https://mlsanity.com/paper/2511.02152/) (2025)
- [Unsupervised Multi-modal Feature Alignment for Time Series Representation Learning](https://mlsanity.com/paper/2312.05698/) (2023)
- [INSIGHTS: Demonstration-Based Summaries of Time Series Predictors](https://mlsanity.com/paper/2605.18849/) (2026)
- [Label-Efficient Time Series Classification at Scale: A Dual-Stream OSSE-LSTM with Counterfactual Attribution](https://mlsanity.com/paper/2610.02704/) (2026)

## Abstract (arXiv)

Recent few-shot learning works focus on training a model with prior meta-knowledge to fast adapt to new tasks with unseen classes and samples. However, conventional time-series classification algorithms fail to tackle the few-shot scenario. Existing few-shot learning methods are proposed to tackle image or text data, and most of them are neural-based models that lack interpretability. This paper proposes an interpretable neural-based framework, namely textitDual Prototypical Shapelet Networks (DPSN) for few-shot time-series classification, which not only trains a neural network-based model but also interprets the model from dual granularity: 1) global overview using representative time series samples, and 2) local highlights using discriminative shapelets. In particular, the generated dual prototypical shapelets consist of representative samples that can mostly demonstrate the overall shapes of all samples in the class and discriminative partial-length shapelets that can be used to distinguish different classes. We have derived 18 few-shot TSC datasets from public benchmark datasets and evaluated the proposed method by comparing with baselines. The DPSN framework outperforms state-of-the-art time-series classification methods, especially when training with limited amounts of data. Several case studies have been given to demonstrate the interpret ability of our model.