The Forecast App is a comprehensive tool designed to guide users through the entire forecasting workflow, from raw data to scenario analysis. It is tailored for both beginners and experienced practitioners, allowing the exploration, modeling, and explanation of time series data without requiring advanced knowledge of forecasting techniques.
The app is organized into four main sections —Data, Analyze, Features, and Forecast— which collectively enable users to:
- Upload and explore time series data
- Detect and handle missing values and anomalies
- Transform and preprocess data for improved forecasting performance
- Create and select relevant features, both internal and external
- Fit and optimize a wide range of forecasting models, including classical, machine learning, deep learning, and ensemble approaches
- Evaluate, compare, and explain the models’ predictions
- Generate probabilistic forecasts and business-oriented scenarios
The app provides great flexibility in analysis: interactive visualizations, configurable model parameters, and multiple options for evaluation and explanation allow users to tailor the process to their specific needs.
You can access the stable version at Forecast App.
Follow the user guide to leverage the full potential of the Forecast App to obtain accurate forecasts, understand model behavior, and explore different scenarios for informed decision-making.
Feel free to share the Forecast App on LinkedIn if you find it useful and remember to tag me!
If you find bugs or have suggestions for improvements, please open an issue on GitHub.
If you are interested in who I am and what I do, visit my website.
The source code of the Forecast App is available on GitHub under the MIT license.