# Few-shot Learning for Time-series Forecasting

arXiv: https://arxiv.org/abs/2009.14379 · HTML: https://mlsanity.com/paper/2009.14379/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2020.

## Built on by (most cited first, 16 of 16)

- [A Survey on Time-Series Pre-Trained Models](https://mlsanity.com/paper/2305.10716/) (2023)
- [Long-Range Transformers for Dynamic Spatiotemporal Forecasting](https://mlsanity.com/paper/2109.12218/) (2021)
- [Multimodal Meta-Learning for Time Series Regression](https://mlsanity.com/paper/2108.02842/) (2021)
- [Few-Shot Forecasting of Time-Series with Heterogeneous Channels](https://mlsanity.com/paper/2204.03456/) (2022)
- [Meta-Forecasting by combining Global Deep Representations with Local Adaptation](https://mlsanity.com/paper/2111.03418/) (2021)
- [Cross-Frequency Time Series Meta-Forecasting](https://mlsanity.com/paper/2302.02077/) (2023)
- [Retrieval Augmented Time Series Forecasting](https://mlsanity.com/paper/2505.04163/) (2025)
- [Channel-wise Retrieval for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2604.05543/) (2026)
- [Meta-Learning for Neural Network-based Temporal Point Processes](https://mlsanity.com/paper/2401.15846/) (2024)
- [Meta-Learning for Koopman Spectral Analysis with Short Time-series](https://mlsanity.com/paper/2102.04683/) (2021)
- [Few-Shot Load Forecasting Under Data Scarcity in Smart Grids: A Meta-Learning Approach](https://mlsanity.com/paper/2406.05887/) (2024)
- [A Simple Baseline for Adversarial Domain Adaptation-based Unsupervised Flood Forecasting](https://mlsanity.com/paper/2206.08105/) (2022)
- [Forecasting Early with Meta Learning](https://mlsanity.com/paper/2307.09796/) (2023)
- [Predicting Electricity Consumption with Random Walks on Gaussian Processes](https://mlsanity.com/paper/2409.05934/) (2024)
- [RAxSS: Retrieval-Augmented Sparse Sampling for Explainable Variable-Length Medical Time Series Classification](https://mlsanity.com/paper/2510.02936/) (2025)
- [Exploring the Potential of Probabilistic Transformer for Time Series Modeling: A Report on the ST-PT Framework](https://mlsanity.com/paper/2604.26762/) (2026)

## Abstract (arXiv)

Time-series forecasting is important for many applications. Forecasting models are usually trained using time-series data in a specific target task. However, sufficient data in the target task might be unavailable, which leads to performance degradation. In this paper, we propose a few-shot learning method that forecasts a future value of a time-series in a target task given a few time-series in the target task. Our model is trained using time-series data in multiple training tasks that are different from target tasks. Our model uses a few time-series to build a forecasting function based on a recurrent neural network with an attention mechanism. With the attention mechanism, we can retrieve useful patterns in a small number of time-series for the current situation. Our model is trained by minimizing an expected test error of forecasting next timestep values. We demonstrate the effectiveness of the proposed method using 90 time-series datasets.