UNITS introduces a unified time series model that addresses challenges in handling diverse time series tasks. It supports various tasks like classification, forecasting, imputation, and anomaly detection through a novel network backbone. UNITS demonstrates exceptional performance across 38 multi-domain datasets compared to task-specific models and natural language-based LLMs. The model showcases zero-shot, few-shot, and prompt learning capabilities on new data domains and tasks. UNITS achieves competitive results in trained tasks and can perform zero-shot inference on novel tasks without additional parameters.
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arxiv.org
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