While deep learning excels at time series forecasting, it typically requires thousands of samples. This paper investigates how few samples are sufficient for reliable prediction and proposes a unified framework integrating meta-learning (MAML, Reptile), neural processes, and diffusion-based augmentation to enable robust forecasting with only 5–20 observations. We establish sample complexity bounds showing attention mechanisms and temporal convolutions achieve superior sample efficiency. Across 89 datasets, meta-learning reduces required samples by 60–80% versus standard deep learning. Our Sample Efficiency Ratio (SER) metric demonstrates that properly regularized deep models outperform statistical baselines (ARIMA, ETS) with as few as 10 samples, challenging the assumption that neural networks are inherently data-hungry.

This paper has systematically investigated the boundaries of sample efficiency in deep learning for time series prediction, demonstrating that with appropriate methodologies—meta-learning, neural processes, and generative augmentation—deep models can achieve reliable forecasting with as few as 5–20 samples. Our unified MDDF framework establishes new benchmarks for minimal data forecasting, outperforming both traditional statistical methods and standard deep learning approaches in the scarce-data regime. The key insight is that sample efficiency is not merely a property of model architecture, but of the entire learning pipeline: how knowledge is transferred across tasks, how uncertainty is quantified, and how limited data is augmented. By learning to learn from minimal data, we expand the applicability of deep forecasting to domains previously considered inaccessible to neural approaches. As IoT deployment accelerates and demand grows for rapid forecasting in new domains—from pandemic response to personalized health monitoring to rare event prediction—the ability to learn from minimal data transitions from academic curiosity to critical infrastructure. Our work provides both theoretical foundations and practical methodologies for this emerging paradigm, establishing that for time series forecasting, few samples are indeed enough when deep learning is properly harnessed.

[1] Malhotra, P., et al. (2019). Meta-Learning for Few-Shot Time Series Classification. arXiv:1909.07155. [2] Shayan, et al. (2024). Forecasting Early with Meta Learning. IEEE International Conference on Data Mining. [3] Xie, Z., & Yu, G. (2024). A Time Series Forecasting Approach Based on Meta-Learning for Petroleum Production under Few-Shot Samples. Energies, 17(8), 1947. [4] Liu, et al. (2024). A Robust Adaptive Meta-Sample Generation Method for Few-Shot Time Series Prediction. Complex & Intelligent Systems. [5] Finn, C., Abbeel, P., & Levine, S. (2017). Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. ICML. [6] Nichol, A., & Schulman, J. (2018). Reptile: A Scalable Meta-Learning Algorithm. OpenAI Technical Report. [7] Garnelo, M., et al. (2018). Neural Processes. ICML Workshop on Theoretical Foundations and Applications of Deep Generative Models. [8] Kim, H., et al. (2019). Attentive Neural Processes. ICLR. [9] Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS. [10] Makridakis, S., et al. (2020). The M4 Competition: 100,000 time series and 61 forecasting methods. International Journal of Forecasting. [11] Bai, S., Kolter, J.Z., & Koltun, V. (2018). An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arXiv:1803.01271. [12] Nie, Y., et al. (2023). A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. ICLR. [13] Oreshkin, B.N., et al. (2020). N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. ICLR. [14] Godahewa, R., et al. (2021). Monash Time Series Forecasting Archive. Neural Information Processing Systems. [15] Dempster, A., Petitjean, F., & Webb, G.I. (2020). ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery.

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