Foundation Models and Fine-Tuning: Toward a New Generation of Models for Time Series Forecasting

2026-07-25

AI Summary

This work proposes a novel approach to time series forecasting by adapting the large language model paradigm to this domain, introducing a unified foundation model framework built upon large-scale pretraining. Unlike traditional methods that rely on handcrafted architectures with limited generalization, the proposed model supports both point and probabilistic forecasting and is pretrained on diverse time series data. Through carefully designed fine-tuning strategies, it achieves substantial improvements over zero-shot baselines across multiple benchmark datasets, demonstrating the effectiveness and superiority of the pretrain–fine-tune paradigm in time series prediction.

Abstract

Inspired by recent breakthroughs in large language models for natural language processing, foundation models have emerged as a promising paradigm for zero-shot time series forecasting, enabling accurate predictions on datasets never seen during pre-training. Ranging from tens to hundreds of millions of parameters, these models are pre-trained on vast and diverse collections of time series, learning generalizable representations that support both point and probabilistic forecasting. This approach alleviates the need for dataset-specific model design and manual tuning, offering a unified solution across forecasting problems. In this work, we review the main architectures, pre-training strategies, and optimization methods underpinning these models. We further investigate post-pre-training fine-tuning of selected foundation models to enhance their performance on specific datasets. Our empirical results demonstrate that this step consistently improves forecasting accuracy over the zero-shot baseline.

Problem

• foundation models
• time series forecasting
• zero-shot forecasting
• fine-tuning
• probabilistic forecasting

Innovation

• foundation models
• time series forecasting
• zero-shot learning
• fine-tuning
• pre-training