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Synthefy provides a time series platform that combines forecasting, scenario simulation, anomaly detection, and synthetic data generation. The core objective is to reason over multivariate time series with exogenous context to support decision automation across domains like retail, telecom, energy, and healthcare. Why this matters
  • Retail: Forecast demand by combining sales with promotions, weather, events, and traffic. Test “what-if” scenarios for price or campaign changes.
  • Telecom: Plan capacity by simulating cell demand spikes around events and stress-testing network algorithms.
  • Healthcare: Augment ECG/PPG datasets and stress-test classifiers using synthetic signals conditioned on patient metadata.
  • Finance/Energy: Run counterfactuals for volatility, demand, and dispatch planning when historical data is sparse.

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Key Capabilities

Use multivariate histories combined with external signals (text, tabular, other time series) to improve predictions. Example: Forecast weekly SKU demand by combining sales history with promotions, weather, and local events.
Ask counterfactual questions and generate trajectories under specified constraints or drivers. Example: Simulate revenue and inventory impact if the price increases by 5% and a holiday event shifts foot traffic by 20%.
Detect deviations by modeling normal behavior with context and surfacing residual patterns. Example: Flag a network KPI spike when traffic and config changes do not explain the deviation.

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Products

Time Series Forecasting and Scenario Simulation

Forecast with multivariate context using the Python SDK and API.

Synthefy Agent

Query forecasting, simulation, and data enrichment through natural language.

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Enterprise Readiness

No training required: pre-trained models work out of the box for common tasks; fine-tuning optional.
Simple APIs, comprehensive SDKs, and Docker containers for any deployment scenario.
Deployment: on-prem or private cloud today; AWS Marketplace listing (Coming Soon).

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Getting Help

Documentation

Comprehensive guides and tutorials to get you started

API Reference

Complete API documentation with examples

Support

Get help from our support team
New to time series forecasting? Check out our Agent, it is the easiest way to get started.