2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) · 2019 · 74 citations · 8 references
ComputingComputer ScienceFinanceForecastingMachine LearningTime Series AnalysisDecision TreesEconomic ForecastingFinancial ForecastingFinancial Time Series AnalysisTime Series ForecastingTime Series PredictionCurrency ForecastingFinancial Time Series ForecastingMachine Learning ClassificationMachine Learning Forecasting ModelsMachine Learning-based ForecastingMachine Learning-based Price ForecastingPredictionPredictive Price Modeling
Bitcoin has become increasingly popular among investors. The study aims to forecast Bitcoin prices accurately by evaluating and comparing the predictive performance of various machine learning algorithms. The authors use a historical Bitcoin price dataset (open, high, low, close) and apply decision tree and regression models to predict daily price trends, comparing their accuracies.
Crypto-currency such as Bitcoin is more popular these days among investors. In the proposed work, it is studied to forecast the Bitcoin price precisely considering different parameters that influence the Bitcoin price. This study first handles, it is identified the price trend on day by day changes in the Bitcoin price while it gives knowledge about Bitcoin price trends. The dataset till current date is taken with open, high, low and close price details of Bitcoin value. Exploiting the dataset machine learning module is introduced for prediction of price values. The aim of this work is to derive the accuracy of Bitcoin prediction using different machine learning algorithm and compare their accuracy. Experiment results are compared for decision tree and regression model.
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Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies
Youngbin Kim, Jun Gi Kim, Jae Ho Im et al. · PLoS ONE · 2016 · 288 citations · Full text