Meta-Learning Framework for Effective Few Shot Time Series Prediction

Few shot time series forecasting is a significant challenge in fields such as healthcare, IoT, and finance, where data scarcity limits the effectiveness of traditional predictive models. Conventional methods often struggle to capture complex temporal patterns and generalize effectively with limited training data. To address this issue, this paper presents a novel meta-learning-based framework designed to enhance forecasting accuracy and adaptability in data-constrained scenarios. The framework leverages knowledge from related tasks through meta-training, enabling rapid adaptation to new tasks with minimal data. This work integrates attention mechanisms to identify salient temporal dependencies and task-specific adaptation layers to improve flexibility and handle task heterogeneity. By extracting and transferring shared patterns across tasks, the framework achieves robust generalization and efficient learning. Comprehensive experiments conducted on diverse real-world datasets demonstrate significant improvements in prediction accuracy and adaptability compared to traditional approaches and meta-learning baselines. The results highlight the proposed method's ability to generalize across domains, offering a practical solution for scenarios characterized by dynamic environments and limited data availability. This work underscores the potential of meta-learning to advance time series forecasting, supporting its broader application in resource-constrained and high-impact fields such as personalized healthcare, predictive maintenance, and financial analysis.

• Publication year

  2025

• Venue

  2025 IEEE 5th International Conference on Power, Electronics and Computer Applications (ICPECA)

• Publication date

  2025-01-17

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/ICPECA63937.2025.10928851
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Automated Vehicle Platooning: A Two-Stage Approach Based on Vehicle-Road Cooperation

  2024cited by this paper
• Short-term wind speed forecasting based on long short-term memory and improved BP neural network

  2022cited by this paper
• Load forecasting of district heating system based on informer

  2022cited by this paper
• A GAN-Based Self-Training Framework for Unsupervised Domain Adaptive Person Re-Identification

  2021cited by this paper
• Monash Time Series Forecasting Archive

  2021cited by this paper
• Parallel spatio-temporal attention-based TCN for multivariate time series prediction

  2021cited by this paper
• Self-Attention ConvLSTM for Spatiotemporal Prediction

  2020cited by this paper
• Time-series forecasting with deep learning: a survey

  2020cited by this paper
• Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections

  2020cited by this paper
• On Data Augmentation for GAN Training

  2020cited by this paper
• Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform

  2019cited by this paper
• The UCR time series archive

  2018cited by this paper

• Meta-learning to Address Data Shift in Time Series Classification

  2026cites this paper