TimeFound: A Foundation Model for Time Series Forecasting

• URL: http://arxiv.org/abs/2503.04118v1
• Date: Thu, 06 Mar 2025 05:55:45 GMT
• Title: TimeFound: A Foundation Model for Time Series Forecasting
• Authors: Congxi Xiao, Jingbo Zhou, Yixiong Xiao, Xinjiang Lu, Le Zhang, Hui Xiong,
• Abstract summary: TimeFound is an encoder-decoder transformer-based time series foundation model.<n>We use a multi-resolution patching strategy to capture complex temporal patterns at multiple scales.
• Score: 33.57877080300831
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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