Meta-Learning for Few-Shot Time Series Classification

TL;DR

This paper introduces a novel meta-learning approach using residual convolutional neural networks for few-shot time series classification, enabling quick adaptation to new tasks with limited labeled data across diverse domains.

Contribution

It is the first to apply meta-learning pre-training to time series classification, overcoming class number constraints and setting new benchmarks on 41 datasets.

Findings

Outperforms strong baselines on 41 datasets

Enables rapid adaptation to new tasks with few samples

First use of meta-learning pre-training for TSC

Abstract

Deep neural networks (DNNs) have achieved state-of-the-art results on time series classification (TSC) tasks. In this work, we focus on leveraging DNNs in the often-encountered practical scenario where access to labeled training data is difficult, and where DNNs would be prone to overfitting. We leverage recent advancements in gradient-based meta-learning, and propose an approach to train a residual neural network with convolutional layers as a meta-learning agent for few-shot TSC. The network is trained on a diverse set of few-shot tasks sampled from various domains (e.g. healthcare, activity recognition, etc.) such that it can solve a target task from another domain using only a small number of training samples from the target task. Most existing meta-learning approaches are limited in practice as they assume a fixed number of target classes across tasks. We overcome this limitation in order to train a common agent across domains with each domain having different number of target classes, we utilize a triplet-loss based learning procedure that does not require any constraints to be enforced on the number of classes for the few-shot TSC tasks. To the best of our knowledge, we are the first to use meta-learning based pre-training for TSC. Our approach sets a new benchmark for few-shot TSC, outperforming several strong baselines on few-shot tasks sampled from 41 datasets in UCR TSC Archive. We observe that pre-training under the meta-learning paradigm allows the network to quickly adapt to new unseen tasks with small number of labeled instances.