focuses on predicting cryptocurrency price movements (e.g., Bitcoin, Ethereum) and modeling their market volatility using time series forecasting techniques like LSTM and ARIMA, and financial risk models like GARCH

his repository contains a research project focused on forecasting Bitcoin (BTC‑USD) prices using the ARIMA (AutoRegressive Integrated Moving Average) model within a machine learning framework. The project explores the challenges of cryptocurrency volatility and demonstrates how ARIMA can be applied to time‑series data for financial forecasting.

This project showcase time series forecasting of cryptocurrency using LSTM (long short term memory) deep learning model, along with volatility analysis to access market risks.

This project analyzes historical cryptocurrency market data to identify trends, volatility, and patterns using time-series techniques. Models such as ARIMA, SARIMA, Prophet, and LSTM are applied for price forecasting, with insights visualized through interactive dashboards.

This project analyzes historical cryptocurrency market data to identify trends, volatility, and patterns using time-series techniques. Models such as ARIMA, SARIMA, Prophet, and LSTM are applied for price forecasting, with insights visualized through interactive dashboards.

The "Time Series Forecasting for Bitcoin Price Prediction" project aims to harness the power of data analysis and predictive modeling to estimate the future value of Bitcoin, a prominent and highly volatile cryptocurrency.

This project presents a robust machine learning pipeline for forecasting next-day cryptocurrency volatility using an XGBoost model. The solution includes a reproducible data workflow, model training with time-series validation, and an interactive Streamlit application for real-time prediction and risk regime classification.

Python app scrapes CoinMarketCap for cryptocurrency data, handles missing values, visualizes metrics in GUI with time series graphs, correlation matrix, growth rates, volatility. LSTM model forecasts prices. Uses pandas, scikit-learn, tensorflow, beautifulsoup4, matplotlib, tkinter.

Python app scrapes CoinMarketCap for cryptocurrency data, handles missing values, visualizes metrics in GUI with time series graphs, correlation matrix, growth rates, volatility. LSTM model forecasts prices. Uses pandas, scikit-learn, tensorflow, beautifulsoup4, matplotlib, tkinter.

This project explores advanced time series techniques—namely ARIMA, GARCH, Granger Causality, and Vector Error Correction Models (VECM)—to forecast the volatility and market trends of two major cryptocurrencies: **Bitcoin** and **Ethereum**.

Cryptocurrency markets are highly volatile and non-stationary. This project develops a robust time-series forecasting pipeline to predict Bitcoin prices over 7, 15, and 30-day horizons using statistical, machine learning, and deep learning models.

This project performs time series analysis on cryptocurrency data to predict future price trends. It collects historical and real-time data, preprocesses it, and applies forecasting models such as ARIMA, LSTM, and Prophet. An interactive dashboard built using Streamlit visualizes price trends, volatility, and predictions to help traders.

Crypto Time Series Analysis project using Python and Power BI to study and forecast cryptocurrency prices. Includes data collection, EDA, and modeling with ARIMA, SARIMA, Regression, and LSTM. Final insights are shown in 10+ interactive Power BI dashboards with trends, volatility, and performance metrics.

This repository contains a complete workflow for analyzing historical cryptocurrency price data, identifying trends and volatility patterns, and building a time-series forecasting model using Long Short-Term Memory (LSTM) networks. The project combines exploratory data analysis (EDA) with deep learning to provide insights into past market behavior

This project explores **deep learning–based time series forecasting** for cryptocurrency prices using **daily OHLCV data**. We compare **Simple RNN** and **LSTM** architectures on **BTC and ETH**, highlighting how model complexity interacts with market volatility.we fetched data from 2021 till 2026

This project uses a 1D Convolutional Neural Network (CNN) to predict Bitcoin prices from historical data (open, close, high, low, volume). Built with Python, TensorFlow, and Keras, the model captures temporal patterns, offering promising results for time-series forecasting in volatile cryptocurrency markets.