This repository implements an XGBoost model for predicting the prices of financial instruments, such as stocks and cryptocurrencies. Using gradient boosting techniques, it aims to capture patterns in price movements, enhancing prediction accuracy across various datasets.
End-to-end market capitalization forecasting system using TimeSeriesTransformer, ARIMA and RNN models with automated data pipeline from Yahoo Finance
This repository implements a WaveNet model for predicting financial instrument prices, such as currencies, stocks, and cryptocurrencies, using advanced AI techniques like gradient boosting to capture intricate patterns in price movements.
This repository implements the KNeighbors Regressor (KNN) model for predicting financial instrument prices such as stocks, currencies, and cryptocurrencies. It leverages gradient boosting techniques to improve accuracy by capturing complex patterns in price movements.
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