Actionable insights, from research-grade forecasting.

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OpenForecast builds open-source forecasting tools and provides consulting and training in demand forecasting and inventory management. Our packages are the rigorous engine behind that work — grounded in peer-reviewed research, refined over a decade, and used by analysts worldwide.

Packages

smooth — forecasting that plans around uncertainty

State-space time series forecasting for R and Python, built on the ADAM framework that unifies exponential smoothing (ETS), ARIMA, and regression in a single model. It produces full probabilistic forecasts rather than single point predictions, handles multiple seasonalities and short histories, and — crucially for supply chain — models the intermittent demand that standard tools get badly wrong.

# R
install.packages("smooth")

# Python
pip install smooth

GitHub · CRAN · PyPI

greybox — the model-building and evaluation toolkit

Companion tools for R that bring the real drivers of demand into the model: regression with information-criteria-based variable selection, explanatory forecasting, and honest forecast evaluation. This is how you explain why demand moves — promotions, pricing, seasonality — instead of extrapolating from history alone.

# R
install.packages("greybox")

# Python
pip install greybox

GitHub · CRAN · PyPI

What these solve

• Forecasts you can plan around — honest uncertainty around every forecast, so safety stock can be set deliberately instead of guessed.
• The hard cases, handled — intermittent and slow-moving items, spare parts, and short data histories where bigger models fail.
• Demand explained, not just extrapolated — bring promotions, pricing, and seasonality into the model as real drivers.
• Glass-box, not black-box — transparent, interpretable models you can explain to finance and stand behind.
• Built to scale — automated model selection across thousands of series, in minutes.

Consulting & training

The packages are freely available, and that transparency is the point. Our value is in applying them: turning your demand data and ERP into working forecasting and replenishment decisions — correcting for stockout-censored sales, capturing your demand drivers, and building models around how your business actually runs. We also run practitioner training in forecasting, inventory management, and analytics.

Website: openforecast.org · Email: mail@openforecast.org · LinkedIn: OpenForecast

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_{Methodology: ADAM (Svetunkov, 2023). Built and maintained by the OpenForecast team.}