RyanLucas3/MacroRandomForest
A modification of traditional random forest for time-series forecasting
This project helps economists and financial analysts make more accurate macroeconomic forecasts and understand the evolving relationships between economic variables. You provide historical time-series data for key economic indicators, and it outputs predictions for future values, along with interpretable, time-varying coefficients that show how different factors influence the forecast over time. It's designed for professionals who need to predict economic trends and also explain the underlying dynamics.
No commits in the last 6 months. Available on PyPI.
Use this if you need to forecast macroeconomic time series with improved accuracy and gain insights into how the influence of different economic factors changes over time.
Not ideal if you are looking for a simple, black-box prediction model without requiring interpretability of the underlying economic relationships.
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Jupyter Notebook
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Last pushed
Apr 16, 2024
Commits (30d)
0
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