We present TimeFound, an encoder-decoder transformer-based time series foundation model for out-of-the-box zero-shot forecasting. To handle time series data from various domains, TimeFound employs a multi-resolution patching strategy to capture complex temporal patterns at multiple scales. We pre-train our model with two sizes (200M and 710M parameters) on a large time-series corpus comprising both real-world and synthetic datasets. Over a collection of unseen datasets across diverse domains and forecasting horizons, our empirical evaluations suggest that TimeFound can achieve superior or competitive zero-shot forecasting performance, compared to state-of-the-art time series foundation models.
View on arXiv@article{xiao2025_2503.04118,
title={ TimeFound: A Foundation Model for Time Series Forecasting },
author={ Congxi Xiao and Jingbo Zhou and Yixiong Xiao and Xinjiang Lu and Le Zhang and Hui Xiong },
journal={arXiv preprint arXiv:2503.04118},
year={ 2025 }
}