bitcoinforecasting

Research project 6620

https://github.com/am2fq/bitcoinforecasting

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
  • Academic publication links
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (5.7%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Research project 6620

Basic Info
  • Host: GitHub
  • Owner: am2fq
  • License: mit
  • Language: Python
  • Default Branch: main
  • Size: 113 KB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created almost 3 years ago · Last pushed about 2 years ago
Metadata Files
Readme License Citation

README.md

BitcoinForecasting

Research project 6620

Abstract

Bitcoin is known for its high volatility, which makes it challenging to accurately predict future prices. In this study, we aim to forecast Bitcoin prices for a month by incorporating exogenous variables, specifically the interest rate and recession probability. Our primary objective is to explore whether these variables have a positive impact on the prediction of Bitcoin prices. We used two popular time series forecasting models: Long Short-Term Memory (LSTM) and Facebook Prophet. Our approach involves exploring the impact of these exogenous variables on the performance of the models and comparing their results through plots and cross-validation. We trained the models using historical Bitcoin price data along with exogenous variables and evaluated their performance on a test dataset. Our results indicate that LSTM outperforms Facebook Prophet in terms of Bitcoin price prediction accuracy. This is because, while Facebook Prophet is optimized for statistical forecasting modeling, LSTM has the capability to learn intricate patterns and relationships given the right architecture with sufficient neurons. Importantly, we demonstrate that incorporating interest rates and recession probabilities significantly enhances the predictive capability of our models. Our findings suggest that changes in interest rates and recession probabilities have an impact on Bitcoin prices, and our models perform better when equipped with this valuable information.

Owner

  • Name: Adel Mahfooz
  • Login: am2fq
  • Kind: user
  • Location: USA

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Mahfooz"
  given-names: "Adel"
  orcid: "https://orcid.org/0000-0000-0000-0000"
title: "BitcoinForecasting"
version: 1.0.0
doi: 10.5281/zenodo.1234
date-released: 2023-04-18
url: "https://github.com/am2fq/BitcoinForecasting"

GitHub Events

Total
Last Year