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Sensible Machine Learning (ML)

Data Science for FP&A

Unleash the power and sophistication of Data Science across Finance and Operations.

Unleash the power and sophistication of Data Science across Finance and Operations.

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Unleash the power of AI across enterprise planning processes by accelerating time to value for ML forecasting at a fraction of the cost of traditional methods.



Empower enterprise planning processes with scalable AI purpose-built for Finance, Operations and Data Science teams to build, deploy and consume time-series models inside of OneStream.



Evolve enterprise planning processes by making ML forecasting easy for FP&A and operational analysts. Break down the traditional barriers of embracing AI/ML and enrich organizational collaboration.

Key Features

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Time Series ML Forecasting for FP&A

Provides FP&A and Business Analysts with a guided experience to build, deploy and consume time-series ML models all in a single, unified platform.

  • Pre-built systematized processes and infrastructure conquer the traditional complexities of disparate development tools.
  • Automated state of the art data science process and techniques that are typically done manually by teams of data scientists.
  • AI Engine automatically adapts to generate the best forecast possible for the training data provided.

Auto-Generate Thousands of Demand Forecasts

Break down the traditionally high barriers with time series machine learning models at scale to optimize planning across the enterprise.

  • Create thousands of daily and/or weekly demand planning ML forecasts across products and locations.
  • Capture business intuition to inform models and increase model performance rather than relying on generalized “top-down” models.
  • Unify and align demand plans with driver-based sales, material costs inventory, and labor plans across the P&L, Balance Sheet and Cash Flow.

Continuous Model Monitoring & Retraining

Increase forecast accuracy and performance by leveraging ML scenarios across reports, visualizations, forms and dashboards.

  • Reduce forecast bias with ML scenarios, compare against human forecasting to drive better dialogue and collaboration.
  • Monitor model health and performance over time; built-in model retraining ensures continuous forecasting and scenario analysis.
  • Drill-back and back testing capabilities create transparency, trust and confidence in budgets, plans, and forecasts.


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