Few-shot Learning for Time-series Forecasting

Abstract

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.

References in corpus (12)

• Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
• Prototypical Networks for Few-shot Learning
• Meta-SGD: Learning to Learn Quickly for Few-Shot Learning
• Towards a Neural Statistician
• Hierarchically Structured Meta-learning
• Nonlinear Systems Identification Using Deep Dynamic Neural Networks
• One-Shot Generalization in Deep Generative Models
• Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions
• The Variational Homoencoder: Learning to learn high capacity generative models from few examples
• Recurrent Attentive Neural Process for Sequential Data
• Recurrent Neural Processes
• Meta Learning with Relational Information for Short Sequences

Cited by in corpus (3)

• Long-Range Transformers for Dynamic Spatiotemporal Forecasting
• Meta-Learning for Koopman Spectral Analysis with Short Time-series
• Meta-Forecasting by combining Global Deep Representations with Local Adaptation