Uses of Package
com.quantfinlib.ml

Packages that use com.quantfinlib.ml

Package	Description
com.quantfinlib.ml	Statistical learning models for market data, in plain Java.

Classes in com.quantfinlib.ml used by com.quantfinlib.ml

Class	Description
AnomalyDetector.Anomaly
GradientBoostedRegressor	Gradient-boosted regression over decision stumps (XGBoost-style additive boosting with squared-error loss), implemented in pure Java with no dependencies.
IntradayLiquidityForecaster	Intraday liquidity forecasting: accumulates per-bucket volumes across days into a seasonal profile (e.g. 24 hourly buckets) to predict when liquidity peaks — London open, the London/New York overlap, etc.
MarketImpactPredictor	ML market impact prediction: learns realized impact (bps) from order and book features using gradient-boosted trees, and estimates the probability a marketable order sweeps through the visible top of book.
RegimeDetector.RegimeModel
VolatilityForecaster	Machine Learning Risk Forecasting: predicts forward realized volatility from a return series using gradient-boosted trees over engineered features (multi-horizon realized vol, momentum, and shock magnitude), and maps the forecast to a 0-100 risk score.
VolatilityForecaster.Validation	Out-of-sample comparison: RMSE (per-period vol units) of the model and of the two baselines over the same forecast dates.