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Zeus is a tuning-free Time Series Foundation Model (TSFM) that excels in various analysis tasks without the need for task-specific fine-tuning. It addresses the challenges of multi-task generalization by integrating a multi-scale Transformer architecture that balances fine-grained fidelity with computational efficiency, alongside a novel Multi-Objective Temporal Masking (MOTM) strategy to cater to diverse task requirements. Experimental results across five tasks reveal that Zeus achieves competitive performance, highlighting its promise as a versatile solution for time series analysis.
Zeus achieves superior time series analysis performance without any task-specific tuning, challenging the norm of fine-tuning for diverse tasks.
We present Zeus, a unified tuning-free Time Series Foundation Model (TSFM) that delivers superior performance across diverse analysis tasks without any task-specific fine-tuning. Unlike prior studies that primarily focus on zero-shot forecasting but require task-specific tuning for other tasks, Zeus bridges this gap by addressing two fundamental challenges in multi-task generalization. First, to reconcile point-level granularity with long-sequence scalability, Zeus incorporates a multi-scale Transformer featuring point-wise tokenization and a U-shaped hierarchy, effectively balancing fine-grained fidelity with computational efficiency. Second, to accommodate varying inductive biases across different tasks, Zeus introduces Multi-Objective Temporal Masking (MOTM), a unified strategy that supports heterogeneous tasks (e.g., extrapolation, interpolation, and global abstraction) within a single framework. Extensive experiments across five representative tasks demonstrate that Zeus consistently achieves competitive results in tuning-free settings, underscoring its potential as a general-purpose TSFM.
