AI Frameworks · microsoft

TimeCraft

TimeCraft is a Microsoft Research diffusion-based framework for generating synthetic time series data across diverse domains (energy, finance, healthcare, transportation). It combines cross-domain generalization, text-based control, and downstream task optimization to produce realistic, controllable synthetic temporal data without retraining for new domains.

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Source: GitHub — github.com/microsoft/TimeCraft

1.1k

GitHub stars

Forks

Python

Primary language

MIT

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	microsoft/TimeCraft
Owner	microsoft
Primary language	Python
License	MIT — OSI-approved
Stars	1.1k
Forks	62
Open issues	6
Latest release	Unknown
Last updated	2026-02-12
Source	https://github.com/microsoft/TimeCraft

What TimeCraft is

TimeCraft uses a diffusion model with semantic prototypes to learn domain-invariant temporal patterns (trends, seasonality) via a universal latent space. A lightweight Prototype Assignment Module (PAM) performs few-shot domain adaptation via "domain prompts." Extensions include CaTSG (causal constraints), OATS (online augmentation for foundation models), and MN-TSG (continuous-time generation from irregular observations). Text conditioning uses multi-agent systems; target-aware generation leverages influence functions to optimize for downstream task performance.

Quickstart

Get the TimeCraft source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/microsoft/TimeCraft.git2cd TimeCraft3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Synthetic data augmentation for data-scarce domains

Generate high-fidelity time series in healthcare, finance, or energy sectors where collecting real data is expensive, time-consuming, or restricted by privacy regulations. Few-shot adaptation enables rapid bootstrap to new domains without full retraining.

Privacy-preserving data sharing and analysis

Produce non-identifiable synthetic time series that retain statistical and causal properties of sensitive datasets, enabling safer collaboration, regulatory compliance, and research without exposure of real individual records.

Task-aware model training and stress-testing

Generate synthetic samples optimized for specific downstream tasks (forecasting, anomaly detection, ICU prediction) using influence functions to guide data generation toward improving model performance. Test robustness in risk-free simulation environments.

Implementation considerations

Project is very young (created Jan 2025, last push Feb 2026); stability and long-term API/module compatibility are Unknown. Monitor for breaking changes as research evolves.
No formal release versioning (latestRelease: none). Code likely evolves alongside research publications; pin specific commits for reproducibility in production.
Requires GPU and significant compute for training diffusion models. Inference cost, latency, and memory footprint for real-time or large-scale generation are not clearly documented.
Three extensions (CaTSG, OATS, MN-TSG) are recent additions; integration complexity and interdependencies are unclear. Verify which modules suit your use case.
Few-shot adaptation (PAM) and text conditioning require careful tuning and validation on your domain. No pre-trained models for specific industries documented.

When to avoid it — and what to weigh

Real-time streaming or latency-critical systems — TimeCraft is a research framework focused on batch generation quality, not optimized for low-latency or streaming inference. No data on inference speed or production deployment patterns.
Requirement for certified/validated synthetic data pipelines — No evidence of FDA, SOC 2, or industry-specific validation frameworks. Use cases requiring regulatory sign-off or audit trails for synthetic data provenance require additional compliance infrastructure.
Minimal Python/ML engineering resources — Requires hands-on Python proficiency, understanding of diffusion models, and likely custom integration to downstream tasks. Not a plug-and-play SaaS product; requires significant engineering effort.
Mission-critical systems without explicit causal validation — CaTSG adds causal constraints, but causality validation depends on domain expertise and model tuning. Automated causality guarantees are not stated. High-stakes decision systems should validate causal assumptions independently.

License & commercial use

MIT License: permissive, allows commercial use, modification, and redistribution with inclusion of license and copyright notice. No patent grant or warranty. Suitable for commercial incorporation with standard attribution.

MIT is a permissive OSI-approved license, broadly compatible with commercial use. However, this is active research code from Microsoft Research; no formal commercial support, service-level agreements, or liability indemnification are stated or implied. Use as-is for internal R&D or consult Microsoft on commercial partnerships or licensing terms.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	High
DEV.co fit	Good
Assessment confidence	High

Security considerations

No security audit, threat model, or vulnerability disclosure process documented. Synthetic data generation itself does not introduce new attack surface, but: - Influence functions and gradient-based generation could leak information about training data or downstream models if not carefully shielded. - Text conditioning system and multi-agent integration introduce external dependencies (NLP services) whose security posture is Unknown. - Recommend independent security review before use in high-assurance contexts. Typical ML model risks (adversarial robustness, bias) apply; no mitigation strategies documented.

Alternatives to consider

Gretel.ai (commercial synthetic data platform)

Purpose-built for privacy-preserving synthetic data with compliance frameworks (HIPAA, GDPR, PCI), enterprise support, and faster deployment. Easier for teams lacking ML expertise, but less flexible for research or domain-specific customization.

OpenAI Time Series API / foundation model adapters

Larger foundation models may offer broader generalization and easier scaling. Fewer research features (causal constraints, influence guidance), but simpler API and potential for commercial support.

Temporal Convolutional Networks (TCN) / Transformer-based seq2seq augmentation

Simpler, well-established baselines for time series generation. Less theoretical sophistication but lower implementation friction and better suited to resource-constrained environments.

Software development agency

Build on TimeCraft with DEV.co software developers

TimeCraft offers advanced diffusion-based generation with few-shot domain adaptation and text-based control. Evaluate fit for your use case with our technical team. Requires Python/ML expertise and GPU infrastructure.

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TimeCraft FAQ

Can I use TimeCraft for real-time data generation in production?

Unknown. No latency, throughput, or streaming architecture documented. TimeCraft is a research framework optimized for batch generation quality. Real-time use requires custom optimization and load testing.

Does TimeCraft come with pre-trained models for specific domains (healthcare, finance)?

Not stated. README mentions cross-domain generalization and few-shot adaptation, but no pre-trained checkpoints or model zoo are documented. You likely need to train on your own domain data or adapt from general models.

How much compute (GPU/CPU) is needed to train and run inference?

Not clearly specified. Diffusion models are compute-intensive; exact hardware requirements depend on model size, dataset, and sequence length. Requires hands-on profiling and testing.

Is this MIT-licensed code safe for commercial use?

MIT permits commercial use with attribution. However, this is research code without formal support or warranties. Consult internal legal/compliance teams and consider commercial partnerships with Microsoft Research if regulatory sign-off is needed.

Custom software development services

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