Forecast Pro vs MLJAR AutoML

Forecast Pro creates statistically based business forecasts for use in broader planning systems. Its automatic best-pick algorithm considers seasonal patterns, trends, business cycles, promotional activity, and other factors. Users can forecast at multiple hierarchy levels while working with units, dollars, and other measures at once. Adjustments can be made at any level, with comments and override reports documenting and reconciling changes. The software archives prior forecasts so users can compare them with actual results and review forecast value added across process steps. Data can move through Excel, CSV, text files, or ODBC, and the maker says the product integrates with ERP, MRP, and BI systems. Forecast Pro runs on Windows and can be installed on a client machine or server, including Citrix or Windows Server. The SDK is also available as a Linux shared library, with.NET and Java interfaces. Professional starts at $199 a month, billed annually, with a 12-month minimum. A free trial provides all features for 21 days and ends automatically.

MLJAR AutoML automates supervised machine learning for tabular data, including binary classification, multiclass classification, and regression. Users can work through MLJAR Studio notebooks or use the open-source Python package in scripts and notebooks. Its four modes—Explain, Perform, Compete, and Optuna—have configurable choices for algorithms, validation, metrics, time budget, and explanations. Depending on the selected mode and settings, workflows can preprocess data, select and tune models, engineer and select features, and create ensembles. Experiments include model comparisons, validation results, training times, explanations, and reusable artifacts; summaries are available in Markdown and JSON. Fairness-aware training evaluates models against a chosen metric and threshold and can try sample reweighting, but does not guarantee fairness. The Python package integrates with Mercury for prediction apps supporting individual and batch CSV predictions; apps can be previewed locally, published through MLJAR Platform, or self-hosted with Docker. The package is open source under the MIT license and costs nothing. AutoML is designed for supervised tabular tasks, and reports depend on task and settings.