Multivariate Bitcoin price prediction based on LSTM tuned by hybrid reptile search algorithm
Authors:
- 25 October 2023
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 195-198
Abstract
This study focuses on analyzing historical data to forecast future trends in Bitcoin prices due to its influence on the business landscape. Its high volatility attracted attention to understanding the influencing factors for its price. This paper presents an empirical investigation using time-series data of various exogenous and endogenous variables. Closing prices of Bitcoin and Ethereum, along with the daily volume of Bitcoin-related tweets are examined for Bitcoin closing price prediction by a long-short term memory (LSTM) network, fine-tuned by a hybrid adaptive reptile search algorithm. The analysis covers a three-year period, in which data is divided into training, validation, and testing sets. Comparative analysis against LSTM networks tuned by other high-performing metaheuristic algorithms demonstrates that the novel approach outperforms competitors in terms of standard regression metrics.Keywords
This publication has 26 references indexed in Scilit:
- A Deep Learning-based Cryptocurrency Price Prediction Scheme for Financial InstitutionsJournal of Information Security and Applications, 2020
- Enhanced Flower Pollination Algorithm for Task Scheduling in Cloud Computing Environment2020
- Firefly Algorithm2020
- A Comprehensive Survey on Portfolio Optimization, Stock Price and Trend Prediction Using Particle Swarm OptimizationArchives of Computational Methods in Engineering, 2020
- Wireless Sensor Networks Life Time Optimization Based on the Improved Firefly Algorithm2020
- Financial Time Series Prediction Based on Deep LearningWireless Personal Communications, 2017
- SCA: A Sine Cosine Algorithm for solving optimization problemsKnowledge-Based Systems, 2016
- On the performance of artificial bee colony (ABC) algorithmApplied Soft Computing, 2008
- Learning to Forget: Continual Prediction with LSTMNeural Computation, 2000
- Long Short-Term MemoryNeural Computation, 1997