The Simulacrum: Decision-Theoretic Pretraining for Near-Optimal Time-Series Forecasting and Inference
arXiv:2606.27711v1 Announce Type: new Abstract: We introduce a neural network-based framework for learning time series estimators through a process we term decision-theoretic pretraining. Analysts specify a generative world, a distribution over data-generating processes, and a target decision objective. A neural network trained on stratified simulations from this world approximates the corresponding optimal decision rule, yielding a neural estimator that provides forecasts, parameter estimates, predictive intervals, or model-selection for zero-shot inference on previously unseen time series. T
This development represents a significant step forward in applying decision theory to neural networks for time-series forecasting, leveraging advancements in deep learning to improve predictive modeling reliability.
This framework could lead to more robust and accurate AI models for predictive analysis across various domains, offering a pathway to zero-shot inference for complex time-series data.
Traditional bespoke time-series modeling approaches may be augmented or replaced by a more generalized, pre-trained neural estimator, leading to more efficient and reliable forecasting.
- · AI/ML researchers
- · Financial institutions (algorithmic trading)
- · Supply chain logistics companies
- · Predictive maintenance industries
- · Traditional statistical modeling consultancies
- · Domain-specific time-series software vendors
- · Companies reliant on human expert judgment for forecasting
Improved accuracy and efficiency in time-series forecasting and inference across numerous industries.
Reduced need for extensive domain-specific feature engineering, accelerating AI adoption in new areas.
Enhanced automation of decision-making processes based on more reliable AI predictions, potentially impacting white-collar workflows.
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Read at arXiv cs.LG