This guide outlines standardized procedures for data processing and model development in time series prediction based on transformers. Transformers here refer to the Artificial Intelligence (AI) model architecture, not the power transformer device. The framework applies to time series prediction tasks in finance, power systems, transportation, and other mission-critical domains. Key provisions include: (1) This guide addresses the construction, training, optimization, validation, deployment, and monitoring of time series prediction models based on transformers. It proposes standardized data schemas, traceable preprocessing pipelines, and quantifiable quality thresholds to help guarantee data validity, consistency, and fitness-for-purpose across training, validation, and inference stages. (2) It prescribes evidence-based practices for model interpretability, uncertainty quantification, failure mode analysis, and iterative retraining. These practices help ensuring operational stability, auditability, and long-term maintainability.
- Standard Committee
- CIS/SC - Standards Committee
- Status
- Active PAR
- PAR Approval
- 2026-03-26
Working Group Details
- Society
- IEEE Computational Intelligence Society
- Standard Committee
- CIS/SC - Standards Committee
- Working Group
-
D&MTSP/P3950 - Data and Model for Time Series Prediction
- IEEE Program Manager
- Sandra Maru
Contact Sandra Maru - Working Group Chair
- Tianjiao Pu
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