Mix, Don't Pick: Why Synthetic Corpus Composition Matters for Time Series Foundation Model Pretraining
arXiv:2606.09912v1 Announce Type: new Abstract: Choosing the wrong synthetic generator for time-series foundation model pretraining is costly: under identical training budgets, the best and worst generators produce up to a $2\times$ gap in forecasting error, yet the field has no principled way to make this choice. The problem is compounded by the fact that generator rankings are not stable across architectures: across 11 generator families evaluated on Chronos-T5-Mini and Moirai-Small trained from scratch, we find that which generators are useful depends on the model architecture. Rather than
The proliferation of foundation models for various domains, including time series, necessitates efficient pretraining methods, making research into synthetic data generation crucial for performance and cost optimization.
Optimizing the pretraining of time series foundation models directly impacts their accuracy and deployment costs, holding significant implications for sectors reliant on forecasting, from finance to logistics and infrastructure management.
The understanding that synthetic data generation methods for time series models are highly architecture-dependent and that mixing, rather than picking, is a more robust strategy for pretraining, changes the approach to model development.
- · AI model developers
- · Cloud computing providers
- · Industries relying on time series forecasting
- · Data science platforms
- · Companies with suboptimal pretraining pipelines
- · Developers using 'one-size-fits-all' synthetic data approaches
Improved performance and efficiency of time series foundation models across various applications.
Reduced computational costs for developing and deploying sophisticated forecasting and anomaly detection systems.
Acceleration of AI agent development that relies on accurate temporal prediction for autonomous decision-making.
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Read at arXiv cs.LG