RANDOM_FOREST

Random Forest (RF) is an ensemble machine learning method based on decision trees. It can be used for time series forecasting by transforming the data into a supervised learning format.

Unlike ARIMA or LSTMs, Random Forest does not assume any underlying statistical relationship in the data and can capture non-linear patterns effectively.

How Random Forest Works for Time Series

✅ Bootstrapping → Trains multiple decision trees on different samples of the dataset

✅ Feature Selection → Uses historical time series values as input (lags, rolling averages, etc.)

✅ Ensemble Predictions → Averages the predictions of multiple trees to reduce overfitting

• Random Forest does not inherently capture time dependencies (like LSTMs) but works well with feature engineering