For this project, I used Bitcoin's daily closing market price dataset from Jan 2012 to March 2021 Kaggle. This work's main objective includes explaining how to analyze a time series and forecast its values using ARIMA and GARCH models.

This project implements a Transformer-based time series prediction model to forecast 6-hour future returns of Bitcoin (BTC/USDT) using Binance OHLCV data

This project analyzes historical Bitcoin price movements using machine learning techniques and time series forecasting models. It utilizes a dataset containing Bitcoin news headlines and price data over time. Various statistical and machine learning methods, such as ARIMA, Prophet, and feature engineering, are used to predict future Bitcoin prices

This is a Bitcoin_time_series_forecast_project for predicting the the future price of bitcoin

Time Series Project on Forecasting Bitcoin Prices

Knowledge Showcase Time Series Project: Bitcoin Price Forecasting > Project Goal: Forecast future Bitcoin prices using historical data and present your findings clearly with plots and insights.

Bitcoin_Price_Time_Series is a project that focuses on predicting Bitcoin prices based on historical time series data. It was created to practice and improve skills in time series forecasting and machine learning.

In this project I have predicted Bitcoin closing prices upto 14 days in future by time series forecasting using Tensorflow.. fundamentals

A cryptocurrency forecasting project analyzing Bitcoin, Ethereum, Dogecoin, and Cardano prices using ARIMA, Prophet, XGBoost, and Hybrid models. It compares traditional time series forecasting with machine learning for financial prediction accuracy.

This project aims to predict Bitcoin (BTC) prices using historical data and advanced machine learning techniques. The model uses time-series forecasting to analyze Bitcoin price trends and make future predictions.

A fun project to use an ARCH time series process to see whether we can forecast Bitcoin prices using 5 years of historical data. This gives an overview in the theory behind ARCH and its application with Bitcoin forecasting.

The Bitcoin Price Prediction project aims to forecast future Bitcoin prices using historical data and various machine learning techniques. This project leverages time-series analysis and machine learning algorithms to predict the price of Bitcoin based on past trends and patterns.

A machine learning and time series forecasting project to predict Bitcoin prices using models like ARIMA, SARIMA, and XGBoost. Built during on-job training at First Quadrant Labs.

Bitcoin Price Trend and Volatility Analysis with ARIMA-GARCH This project analyzes historical Bitcoin price data using ARIMA for trend forecasting and GARCH for volatility modeling. It includes data preprocessing, time series modeling, volatility forecasting, and visualization of both price predictions and risk bands.

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 repository focuses on predicting Bitcoin prices using time series analysis and machine learning models. It includes data preprocessing, model training, and evaluation steps to forecast Bitcoin prices based on historical data. The project demonstrates the application of statistical and machine learning techniques to cryptocurrency prediction.

Time-series forecasting project for Bitcoin prices using deep learning. Compares feedforward MLP and sequence-based LSTM models to highlight the advantage of temporal learning in volatile financial data. Built with PyTorch and evaluated using standard regression metrics.

This project predicts Bitcoin prices using an LSTM neural network, which is designed to analyze time-series data. The model is trained on historical Bitcoin prices and market indicators to forecast future trends. It includes data preprocessing, model training, and evaluation using Python libraries like TensorFlow and Pandas.

A project that analyzes historical Bitcoin prices to forecast future trends with deep learning and statistical models (LSTM, BiLSTM, ARIMA and Transformer). The Transformer-based approach achieves the highest accuracy, leveraging time-series data for improved financial decision-making.

This repository features a complete time-series forecasting project to predict the price movements of Bitcoin (BTC) against the US Dollar. The core of this project uses a Long Short-Term Memory (LSTM) neural network, a class of recurrent neural networks (RNNs) highly effective for sequence prediction tasks

This project analyzes and predicts Bitcoin prices using historical data. It involves data acquisition, exploration, and preprocessing, followed by building and training an LSTM model for time series forecasting. The model predicts future prices, and the results are visualized. The trained model is also saved

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Semester-Level Time Series Forecasting Project: Bitcoin Prices

Time Series Project focused on forecasting Bitcoin price

Time series Projecting using Prophet's Forecasting system to Forecast Bitcoin prices

Python code for Time Series and Geospatial Data Science CIVE7100 Project on Bitcoin Forecasting

# Bitcoin Price Forecasting This project develops a time series forecasting model for Bitcoin prices and deploys it as a Streamlit web application.

Academic project of BDA(Big Data Analytics) Predictive modeling and anomaly detection on Bitcoin time-series data using Python. Includes data cleaning, feature engineering, and regression-based forecasting with Linear Regression, Random Forest, and XGBoost models.

A mini time series analysis project on Bitcoin (BTC) using Python, yfinance, and ARIMA forecasting.

School project for Capgemini Invent : Use time series analysis and deep learning for bitcoin forecasting