Foundational models, particularly those built on the transformer architecture, represent a state-of-the-art approach for time-series forecasting. These large models are pre-trained on vast amounts of sequential data, enabling them to learn complex patterns, seasonality, and long-range dependencies. They can then be fine-tuned for specific forecasting tasks, often achieving superior accuracy compared to traditional statistical methods or other neural network types. Their ability to handle long input sequences makes them exceptionally well-suited for complex time-series problems.

A. Generative AI: This category's primary purpose is to create new content (e.g., text, images), not to perform predictive analysis on historical numerical data for forecasting.

C. Natural language processing: This field of AI is specifically concerned with understanding and processing human language, not the numerical, time-ordered data characteristic of time-series.

D. Robotic process automation: This technology automates rule-based business processes and digital tasks; it lacks the analytical and predictive capabilities required for forecasting.

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1. Gruver

N.

et al. (2024). "Large Language Models are Zero-Shot Time Series Forecasters." Advances in Neural Information Processing Systems 36 (NeurIPS 2023). Section 1

Paragraph 2 discusses the limitations of existing methods and the potential for large

pre-trained models (i.e.

foundational models) to be adapted for time-series forecasting tasks.

2. Zhou

T.

et al. (2023). "One Fits All: A Generative Foundation Model for Time Series Analysis." Proceedings of the 40th International Conference on Machine Learning

PMLR 202

pp. 41933-41951. The abstract and introduction explicitly propose a "foundation model for time series analysis" to handle diverse datasets and tasks

including forecasting.

3. Rasul

K.

et al. (2024). "Chronos: Learning the Language of Time Series." Amazon Science. This official publication introduces Chronos

a family of pre-trained foundational models specifically designed for time-series forecasting

demonstrating a direct industry application of this concept. Available at: https://www.amazon.science/publications/chronos-learning-the-language-of-time-series

4. Vaswani

A.

et al. (2017). "Attention is All You Need." Advances in Neural Information Processing Systems 30 (NIPS 2017). This paper introduced the Transformer architecture

which is the core technology behind most modern foundational models and has been successfully adapted from its original NLP context to time-series forecasting.