Emerging Trend of Transaction and Investment: Bitcoin Price Prediction using Machine Learning

Loh, Eng Chuen

doi:10.30534/ijatcse/2020/1591.42020

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…Prophet was created to handle planned holidays, missing data, and significant outliers [8]. It may therefore be utilized by non-statisticians to produce conclusions that are generally on par with, if not superior to, those produced by experts.…”

Section: Literature Surveymentioning

confidence: 99%

Empirical Forecasting Analysis of Bitcoin Prices

Tripathy,

Hota,

Mishra

et al. 2024

Int. j. electr. comput. eng. syst. (Online)

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Bitcoin has drawn a lot of interest recently as a possible high-earning investment. There are significant financial risks associated with its erratic price volatility. Therefore, investors and decision-makers place great significance on being able to precisely foresee and capture shifting patterns in the Bitcoin market. However, empirical studies on the systems that support Bitcoin trading and forecasting are still in their infancy. The suggested method will predict the prices of all key cryptocurrencies with accuracy. A number of factors are going to be taken into account in order to precisely predict the pricing. By leveraging encryption technology, cryptocurrencies may serve as an online accounting framework and a medium of exchange. The main goal of this work is to predict Bitcoin price. To address the drawbacks of traditional forecasting techniques, we use a variety of machine learning, deep learning, and ensemble learning algorithms. We conduct a performance analysis of Auto-Regressive Integrated Moving Averages (ARIMA), Long-Short-Term Memory (LSTM), FB-Prophet, XGBoost, and a pair of hybrid formulations, LSTM-GRU and LSTM-1D_CNN. Utilizing historical Bitcoin data from 2012 to 2020, we compared the models with their Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The hybrid LSTM-GRU model outperforms the rest with a Mean Absolute Error (MAE) of 0.464 and a Root Mean Squared Error (RMSE) of 0.323. The finding has significant ramifications for market analysts and investors in digital currencies.

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Section: Literature Surveymentioning

confidence: 99%

Empirical Forecasting Analysis of Bitcoin Prices

Tripathy,

Hota,

Mishra

et al. 2024

Int. j. electr. comput. eng. syst. (Online)

View full text Add to dashboard Cite

show abstract

“…With evolution in cryptocurrency and advances in the creation of centralized and decentralized exchanges, accurate information on prices has become accessible and therefore studies are emerging in this line of research using Neural Networks and Deep Learning to analyze market volatility (Bu & Cho, 2018; Miura et al, 2019), forecast future prices (Betancourt & Chen, 2021b; Bu & Cho, 2018; Ji et al, 2019; Lahmiri & Bekiros, 2019, 2021; Lee, 2020; Li et al, 2020; Livieris et al, 2021; Loh & Ismail, 2020; Lucarelli & Borrotti, 2019; Miura et al, 2019; Nithyakani et al, 2021; Sattarov et al, 2020; Sun et al, 2021; Zanc et al, 2019), and managing portfolios with Bitcoin in an automated way (Betancourt & Chen, 2021a; Jiang & Liang, 2016; Ren et al, 2021; Shi et al, 2019; Sun et al, 2021).…”

Section: Systematic Reviewmentioning

confidence: 99%

Cryptocurrencies trading algorithms: A review

Silva

Hadad

Oliveira

2022

Journal of Forecasting

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This study conducts a bibliometric analysis and systematic review of cryptocurrency trading algorithms to identify existing gaps in the area. From our standpoint, this is the first study to carry out a deep analysis of price forecasts and portfolio management in cryptocurrencies in addition to analyzing the most relevant studies and authors, trend topics of the area, and identifying countries with the most published studies. During our research, we identified some gaps that can be used for further research. Currently, there are approximately 16,000 cryptocurrencies; however, in majority of the papers, the authors have only used the top 10 ranking market capitalization cryptocurrencies, leaving aside potential minor cryptocurrencies. Thus, trading strategies using Big Data can be a potential research topic, considering the greater number of emerging cryptocurrencies.

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“…It obtains a bitcoin price movement classification accuracy of 75%. Limited research has been performed to analyze network influence on overall bitcoin prices [6].…”

Section: Introductionmentioning

confidence: 99%

Analyzing Ola Data for Predicting Price Based Trip Distance Using Random Forest and Linear Regression Analysis

Tarun

Sriramya

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

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The paper aims to create a most efficient and accurate cab fare prediction system using machine learning algorithms and comparing them. The machine learning algorithms are Random forest algorithm and Linear regression and comparing the r-square, mean square error (MSE), Root MSE and Root Mean Squared Logarithmic Error (RMSLE) values. We implement the Random forest and linear regression algorithms to predict the prices of the system and to get the best accuracy when comparing both the algorithms. The algorithms should be efficient to predict the prices of the trips before the starting of the trip. The sample size considered for this work is N=10 for each of the groups considered. Totally it was iterated 20 times for efficient and accurate analysis on prediction of price with G-power in 80% and threshold 0.05%, CI 95% mean and standard deviation. The sample size calculation was done with clincle. Based on the statistical analysis the significance value for calculating the r-square was found to be 0.034. The Random forest algorithm gives a slightly better accuracy rate with a mean r-square percentage of 71.67% and the linear regression has mean r-square value of 70.57%. By this process, the prediction is done for the price prediction of the online cab rental system and the Random forest algorithm gives a better r-square value compared to the Linear regression algorithm.

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Emerging Trend of Transaction and Investment: Bitcoin Price Prediction using Machine Learning

Cited by 7 publications

References 11 publications

Empirical Forecasting Analysis of Bitcoin Prices

Empirical Forecasting Analysis of Bitcoin Prices

Cryptocurrencies trading algorithms: A review

Analyzing Ola Data for Predicting Price Based Trip Distance Using Random Forest and Linear Regression Analysis

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