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.

Easy to follow stock price analysis on Indian stock data

Powerful XRP price forecasting using public data. Stacking ensemble (Bi-GRU/LSTM/CNN-LSTM + LightGBM/XGBoost, RidgeR). Fuses market OHLCV (CCXT), news sentiment & top50 whale activity. No API keys or signups. Easy setup. CPU/GPU-ready. Multi-horizon single run forecasting. Backtests + Predictions visuals: plot_charts & in-depth tensorboard dash

This project, I am constructing a predictive model that can prognosticate gold prices using historical price data and pertinent financial indicators.

EU power market model for system analysis

📘 Home Assignment for the Data Scientist Position (Curves) at Argus Media Group

Using Long-Short Term Memory Neural Networks to forecast and trade Ethereum, a cryptocurrency.

Multi-country energy price forecasting system with event awareness and data quality guards.

✨ Home Assignment for the Data Scientist Position (Curves) at Argus Media Group

AI-powered e-commerce competitor analysis with price forecasting (0.38% MAPE) and sentiment analysis using Chronos, Prophet, and Llama 3.3 70B

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.

Developed a machine learning-based predictive model to forecast Airbnb prices using features like location, amenities, and property type.

This API service is used to forecast product price given the contributing materials that form the final product.

Developed and compared models to forecast hourly electricity load and prices using over nine years of real-world German market data, spanning linear methods (AR, OLS) and machine learning algorithms (Random Forests, Regression Trees).

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 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.

A repository demonstrating the forecasting of control reserve market prices (as MWE). For the experiments, German control reserve market data was used.

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.

A machine learning platform for real-time price and trend forecasting of fashion items using time series analysis, clustering, and GenAI for trend prediction. Features include ARIMA/Prophet forecasting, dynamic pricing models, and interactive dashboards.