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.