Repo containing time series forecasting solutions ranging from N-BEATS, LSTM and autoregressive models trained for predicting stock and bitcoin price.

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

Time Series Forecasting: Predicting Bitcoin Price The cryptocurrency market has seen its rise and fall in the past few years. With a variety of coins being exchanged for real money, it is important to know the trend in the coin price. In this article, we will build a fairly simple LSTM network to predict or forecast the prices of Bitcoin. Obtaining Bitcoin Data There are plenty of open sources available on the internet to extract historical data of Bitcoin prices. The one that I have used below is from https://coinmarketcap.com/.

Predicted Bitcoin Prices using ARIMA Time Series forecasting to monitor real time predicted prices, leading to improved decision-making. Used the volatility of Bitcoin Prices along with the historical data from Kaggle to predict prices in real time.

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 notebook provides a comprehensive approach to time series forecasting by predicting Bitcoin prices using historical price data.

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

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

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 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.

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.

This repository gives a deep understanding about different forecasting techniques to predict the prices of cryptocurrencies using time series analysis. We compare several alternative univariate and multivariate models for forecasting the price of six of the most capitalized series: Bitcoin, Ethereum, Lite coin, Ripple, Dash and Monero.

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

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.

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.

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

Three of the most popular stock analysis methods are technical, fundamental, and sentimental analysis. Perhaps Machine Learning can be used to remove the human element from these methods of analysis. For fundamental analysis, classification models were used to determine if a stock would be a Buy, Sell, or Hold. For technical analysis, time series models were used to forecast the general direction of the stock price. Predicting the future is no easy task. Many have tried and many have failed. But many of us would want to know what will happen next and would go to great lengths to figure that out. Imagine the possibilities of knowing what will happen in the future! Imagine what you would have done back in 2012 when Bitcoin was less than $15 knowing that it would surpass $18,000! Many people may regret not buying Bitcoin back then but how were they supposed to know in the first place? This is the dilemma we now face in regards to Cryptocurrency. We do not want to miss out on the next jump in price but we do not know when that will or will not happen. So how can we potentially solve this dilemma? Maybe machine learning can tell us the answer. Machine learning models can likely give us the insight we need to learn about the future of Cryptocurrency. It will not tell us the future but it might tell us the general trend and direction to expect the prices to move. Let’s try and use these machine learning models to our advantage and predict the future of Bitcoin by coding them out in Python!

Time Series Forecasting with Tensorflow / Predicting the price of Bitcoin

Multivariate Time Series Forecasting with LSTM and Bidirectional LSTM to predict Bitcoin Prices

Predictive Analytics on Time-series data creating ARIMA/VAR model for forecasting the bitcoin prices

Time series forecasting model to predict Bitcoin prices using machine learning and historical market data for trading strategy optimization.

A time series forecasting to predict the Bitcoin's price trend from Nov 2017 to Feb 2018

A time series forecasting project analyzing Bitcoin prices using ARIMA, SARIMA, and GARCH models to predict short-term trends and volatility.

This project focuses on predicting the price of Bitcoin using time series analysis and forecasting methods. The main objective is to build a reliable time series forecasting system that helps understand Bitcoin’s behavior and estimate its future price movements.

LSTM based time-series forecasting model to predict the hourly bitcoin price using six years of hour level data

Time series forecasting project using LSTM and ARIMA models to predict cryptocurrency prices (Bitcoin, Ethereum). Includes data preprocessing, model training, evaluation, and visualization.

This project focuses on predicting Bitcoin prices using historical market data and machine learning techniques. It involves gathering time-series data, pre-processing it, training regression models, and evaluating their performance in forecasting future Bitcoin prices.

This project aims to evaluate the predictive power of the N-BEATS deep learning architecture for forecasting Bitcoin prices using various time series data.

This project aims to demonstrate the application of machine learning techniques for time series forecasting, specifically predicting Bitcoin prices. It utilizes historical price data and chainlet features, which represent transaction patterns on the Bitcoin network.