Microsoft TimeCraft For Synthetic Time-Series Data Generation

Time-series data is the backbone of critical decision-making in sectors such as healthcare, finance, and transportation. However, generating realistic and adaptable synthetic time-series data is a persistent challenge. 

TimeCraft is an open-source framework developed by Microsoft Research Asia that addresses this gap by providing a universal, flexible solution for time-series data generation that caters to diverse industry needs.

Why Synthetic Time-Series Matters

Organizations routinely grapple with data scarcity, privacy issues, and the challenge of simulating rare or hypothetical scenarios. AI-generated synthetic data offers a solution, enabling privacy-preserving datasets for model training and scenario analysis. Yet, many existing solutions struggle to deliver the adaptability, control, security and realism required for practical, high-impact deployment.

TimeCraft’s Three-Pronged Approach to Data Generation

TimeCraft distinguishes itself with three innovative data-generation strategies:

• Few-shot adaptation: Users upload a handful of unlabeled samples from their domain, allowing TimeCraft to learn underlying structural patterns and generate domain-specific data with no retraining or labels necessary.
  
  
• Natural language control: With plain-language prompts (such as “stable early on, followed by sharp fluctuations”), users can describe desired time-series characteristics. TimeCraft then translates these instructions into matching synthetic datasets, making advanced data generation accessible to non-specialists.
  
  
• Task model feedback: By integrating custom predictive models, users enable TimeCraft to dynamically adjust data generation based on real-time model responses, optimizing output for downstream application performance.

These methods can be used independently or in combination, offering unmatched flexibility to meet operational and research objectives.

Unified Framework Across Industries

TimeCraft leverages semantic prototypes or universal representations of time-series structures, to ensure smooth adaptation across domains. The Prototype Assignment Module (PAM) maps sample sequences to these prototypes, guiding the model to produce structurally consistent synthetic data without the need for additional retraining.

This architecture enables rapid deployment across various sectors, from energy to healthcare, and ensures strong generalization to new or rapidly changing data environments.

Text-Controlled Generation: From Description to Data

Often, users know the data patterns they require but lack suitable examples. TimeCraft’s text-to-time-series module solves this by transforming plain-language descriptions into actionable datasets. Its multi-agent system refines user inputs with real-world phrasing and statistical nuance, ensuring generated data accurately mirrors user intent.

This approach democratizes synthetic data creation, empowering users without technical backgrounds to produce highly specific datasets simply by writing a sentence especially useful in data-scarce or fast-moving fields.

Task-Aware Generation: Data That Drives Outcomes

TimeCraft extends beyond realism with task-aware generation, focusing on optimizing synthetic datasets for superior downstream model performance. By incorporating predictive models directly into the generation process and using an influence scoring method, TimeCraft identifies which synthetic samples most enhance model outcomes and iteratively refines its output.

This is crucial for generating rare or high-impact data, such as those required for medical diagnoses or financial risk analysis, making synthetic data a strategic asset for research and business operations.

Open Source and Built for Scale

Engineered for real-world use, TimeCraft supports varied input types, handles complex scenarios, and improves continuously with task-driven feedback. As an open-source project, it welcomes contributions from developers, researchers, and industry partners eager to explore and extend its capabilities.

A Leap Forward in Synthetic Data Generation

TimeCraft marks a significant evolution in time-series data synthesis, combining adaptability, user control, and practicality in a single, universal platform. By empowering organizations to generate realistic and impactful synthetic data, it sets a new benchmark for AI-powered decision-making helping to lower barriers to innovation and safeguarding sensitive information.

Source: Microsoft Research Blog