SVM, GA-SVM, and DFS-BPSO, training results revealed that the RBF-SVM algorithm outperformed others in accuracy. Based on these studies, this research aims to analyze crypto currency price predictions using the Long Short Term Memory, Support Vector Machine, and Polynomial Regression methods.

Abstract—We present a model that can identify artificial increases in crypto-currency prices (called ’pumps’) from publicly available order data. Our dataset consists of 179 24-hour order books from several prominent currencies.

Apr 20, 2022 · Using a time series of daily ether cryptocurrency closing prices, price prediction is accomplished using two machine learning algorithms, namely, linear regression (LR) and support vector machine (SVM).

This study proposes the use of three supervised machine learning methods, namely linear regression, support vector machine, and decision tree, to estimate the price of four prominent cryptocurrencies: Bitcoin, Ethereum, Dogecoin, and Bitcoin Cash.

Apr 30, 2020 · This study suggests a predicting model for blockchain Bitcoin cryptocurrency prices and its profitability trading strategies using machine learning algorithms (ICA-Firefly and SVMs).

Feb 2, 2024 · By utilizing the models of Long Short Term Memory, Support Vector Machine, and Polynomial Regression algorithm for forecasting, a performance comparison is conducted to determine which algorithm model is most suitable for predicting crypto currency prices.

May 25, 2022 · To this end, the authors of this work propose a novel framework that predicts the price of Bitcoin (BTC), a dominant cryptocurrency. For stable prediction performance in unseen price range, the...

The proposed model is to build a machine learning model where the data is used to made to learn about the pattern in the dataset and the machine learning algorithm is used to predict the bitcoin price.

Mar 15, 2025 · This study addresses the challenges of predicting cryptocurrency prices by analyzing two prominent machine learning methodologies: support vector machine (SVM) models and artificial neural networks (ANN).

This research is directed towards predicting volatile price movement of cryptocurrency by analyzing the sentiment on social media and finding the correlation between them. The most efficient combination of machine learning algorithms …