This repository implements an ARIMA model for predicting financial prices such as stocks, currencies, and cryptocurrencies. It focuses on time series forecasting to capture temporal dependencies and improve prediction accuracy across different financial datasets.

This repository implements an SARIMAX model for predicting financial instrument prices (stocks, currencies, cryptocurrencies). The model uses gradient boosting to capture complex price patterns and handle diverse dataset characteristics for accurate price forecasting.

This repository contains an implementation of the LightGBM model for predicting financial instrument prices like stocks, currencies, and cryptocurrencies. It uses gradient boosting to analyze patterns in price data, aiming to enhance the accuracy and reliability of financial predictions.

This repository implements the CatBoostRegressor model for predicting prices of financial instruments like stocks, currencies, and cryptocurrencies. It uses gradient boosting to capture patterns in price movements, improving the accuracy and robustness of price forecasts.

This repository implements a Temporal Convolutional Network (TCN) model for predicting financial instrument prices, including currencies, stocks, and cryptocurrencies. It uses advanced techniques like gradient boosting to improve prediction accuracy and handle diverse datasets effectively.

This repository implements a Random Forest Regressor for price prediction in financial markets, including stocks, currencies, and cryptocurrencies. It uses gradient boosting techniques to improve the model's accuracy and robustness for forecasting financial data across different datasets.

This repository implements the Prophet model for predicting prices of financial instruments like currencies, stocks, and cryptocurrencies. It uses gradient boosting techniques to capture complex patterns in price movements, enhancing forecast accuracy and robustness for financial predictions.

This repository implements an SVR model for predicting prices of financial assets like stocks, currencies, and cryptocurrencies. It uses gradient boosting to capture complex patterns in price data, improving the accuracy of predictions across various datasets.