Foundation Models Revolutionize Time Series Forecasting with Fine-Tuning

Inspired by the success of large language models, foundation models are emerging as a transformative paradigm for time series forecasting. These models, ranging from tens to hundreds of millions of parameters, are pre-trained on vast and varied time series datasets. This pre-training allows them to learn generalizable representations, enabling accurate zero-shot predictions on datasets they have never encountered before, supporting both point and probabilistic forecasting. This approach significantly reduces the need for dataset-specific model design and manual tuning, offering a more unified and efficient solution across a wide array of forecasting challenges. The research provides a comprehensive review of the main architectures, pre-training strategies, and optimization methods that underpin these innovative models. Furthermore, it empirically investigates the impact of post-pre-training fine-tuning on selected foundation models. The results consistently demonstrate that this fine-tuning step leads to significant improvements in forecasting accuracy when compared to the zero-shot baseline. This confirms that while foundation models offer strong out-of-the-box performance, targeted adaptation can further unlock their potential for specific applications.