Commodity forecasting has always been a race against noise and structural change. In volatile markets, relying on a single model or low-frequency aggregates is a recipe for drift. The evidence is clear, combining diverse models tends to beat picking a single winner, as shown by the M4 competition’s emphasis on forecast combinations and hybrid methods (International Journal of Forecasting; review of combinations). Mixed-frequency approaches like MIDAS let you exploit high-frequency signals without crude aggregation, which multiple studies and vendor implementations demonstrate for macro and commodities (EViews on MIDAS, JSS MIDAS toolbox, oil volatility with mixed-frequency inputs). Finally, prioritizing the first step ahead matters, because recursive multi-step forecasts compound early errors, a point documented in the multi-step literature (Taieb and Hyndman).

With that context, here are the eight best commodity forecasting software options for 2025, selected for breadth of models, support for transformations and exogenous variables, and their ability to ingest high-frequency and mixed-frequency data streams.

8 Best Commodity Forecasting Software in 2025

1) Indicio

Indicio is a no-code forecasting platform designed to operationalize broad model evaluation across statistical, ML, Bayesian, and penalized approaches, while automating variable search and transformations against internal and external leading indicators. Its workflow emphasizes mixed-frequency inputs and high-frequency signals, avoiding information loss from aggregation. It is built for business users as much as analysts, so purchase managers and executives can explore driver effects, run what-ifs, and deploy robust forecasts without writing code. Indicio also offers an accuracy uplift guarantee of at least 30 percent, aligning incentives with measurable outcomes.

Pros

1. Broad library with automated evaluation of many models, transformations and variables.
2. Mixed-frequency friendly pipeline and integrated scenario analysis.
3. No-code usability for non-PhDs, yet transparent diagnostics for analysts.

Cons

1. Costs more than open-source packages.
2. You cannot code your own custom models inside the platform.
3. Requires an internet connection.

2) SAS Visual Forecasting and SAS/ETS

SAS Visual Forecasting and SAS/ETS combine large-scale automated time series with advanced econometrics, including state-space, UCMs, VARMAX, and multiple GARCH flavors for volatility, plus ML and hybrid strategies for panel series at scale. The stack is proven in energy and commodities where modeling thousands of hierarchies and related series is routine, and it slots into enterprise governance and IT standards.

Pros

1. Enterprise-grade scalability for thousands of SKUs, contracts, and locations.
2. Rich econometrics including VAR/VECM, state-space, and GARCH models.
3. Seamless integration with the SAS Viya ecosystem for deployment.

Cons

1. License cost and infrastructure footprint can be high.
2. Mixed-frequency modeling requires custom configuration or code.
3. Steep learning curve for non-technical users.

3) EViews

EViews is a long-standing econometrics platform popular in central banks and commodity houses. It natively supports MIDAS regression and even MIDAS-GARCH, allowing users to combine daily or weekly drivers with monthly targets without discarding intra-period information. Its workflow is straightforward for building VARs, ARIMAs, state-space models, and simulations, and it integrates well with spreadsheet-centric teams.

Pros

1. Native MIDAS implementations for mixed-frequency forecasting.
2. Broad econometric coverage including VAR, state-space, and simulation models.
3. Approachable GUI for analysts coming from Excel.

Cons

1. Limited scalability compared with distributed cloud stacks.
2. Some advanced modules require additional add-ins.
3. Less suited for large-scale automation and deployment.

4) MATLAB Econometrics Toolbox with MIDAS toolboxes

MathWorks Econometrics Toolbox covers ARIMA, state space, GARCH, VAR, and more, and the community provides robust MIDAS toolboxes for mixed-frequency regression and volatility modeling. For commodity quants, MATLAB’s matrix ecosystem makes it easy to prototype, backtest, and integrate optimization under real constraints.

Pros

1. Comprehensive econometric and simulation suite with reliable numerical tools.
2. Strong support for community-based MIDAS toolboxes.
3. Excellent visualization and optimization capabilities.

Cons

1. Requires paid licenses for MATLAB and add-ons.
2. Scaling to very large time series sets can be complex.
3. Power users need scripting skills to unlock full potential.

5) Forecast Pro

Forecast Pro focuses on pragmatic, scalable forecasting, with automatic best-pick model selection across exponential smoothing, Box-Jenkins ARIMA, and related families. It is widely adopted by supply chain and manufacturing teams and integrates seamlessly with ERP and planning systems.

Pros

1. Automatic best-pick model selection for large portfolios.
2. Collaboration, hierarchy management, and S&OP-ready reporting.
3. Fast implementation and user-friendly interface.

Cons

1. Limited support for mixed-frequency data.
2. Lacks advanced machine learning methods.
3. Commodity price volatility modeling requires external tools.

6) H2O Driverless AI

H2O Driverless AI automates feature engineering, rolling-window backtesting, and model selection across gradient boosting and other ML methods. It is particularly effective when building ensembles over engineered exogenous drivers and large datasets.

Pros

1. Automated feature engineering and rolling-window evaluation at scale.
2. Broad algorithmic coverage with robust deployment patterns.
3. Handles multiple regressors useful for driver-rich commodity models.

Cons

1. Mixed-frequency handling requires preprocessing.
2. Higher license costs compared with open-source alternatives.
3. Model interpretability depends on analyst expertise.

7) Bloomberg BQuant

BQuant integrates Python and JupyterLab into the Bloomberg ecosystem, allowing analysts to combine terminal data, BQL, and custom models for forecasting and signal research. It’s ideal for near-term price forecasting and event studies.

Pros

1. Tight integration with real-time and historical commodity data.
2. Full Python flexibility for custom modeling.
3. Enterprise sharing and collaboration through Launchpad.

Cons

1. Requires expensive Bloomberg licenses.
2. No turnkey mixed-frequency nodes, coding required.
3. Deployment depends on IT governance setup.

8) Nixtla StatsForecast (Python)

StatsForecast is a high-performance open-source library implementing a wide range of classical time series models optimized for large-scale operations. It’s a strong option for developers building custom commodity forecasting pipelines.

Pros

1. Extremely fast implementations of ARIMA, ETS, Theta, and other models.
2. Supports large-scale forecast portfolios and ensembles.
3. Fully open-source and highly extensible.

Cons

1. Mixed-frequency workflows require custom engineering.
2. Python skills are necessary to implement.
3. Limited built-in tools for volatility or structural models.

Why mixed-frequency and high-frequency data matter in commodities

Commodity markets are driven by information arriving at different cadences, such as daily freight rates, weekly inventories, and monthly macro indicators. Mixed-frequency modeling, especially MIDAS, uses all higher-frequency observations rather than aggregating to the lowest frequency, reducing information loss and improving short-term accuracy. This leads to better first-step forecasts, which is crucial since multi-step forecasts compound early errors. Combining multiple models and weighting them intelligently consistently outperforms relying on a single model.

‍