Prediction of Bitcoin prices with machine learning methods using time series data

Karasu, Seçkin; Altan, Aytaç; Saraç, Zehra; Hacıoğlu, Rıfat

doi:10.1109/siu.2018.8404760

Cited by 74 publications

(35 citation statements)

References 9 publications

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“…Even though Bitcoin prices follow a time series sequence, machine learning models are considered due to their performance reported in the literature (Karasu et al 2018;Chen et al 2020). This approach serves the purpose to measure the relative prediction power of the shallow/deep learning models, as compared to the traditional models.…”

Section: Model Implementation and Resultsmentioning

confidence: 99%

A Gated Recurrent Unit Approach to Bitcoin Price Prediction

Dutta¹,

Kumar

Basu³

2019

SSRN Journal

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In today's era of big data, deep learning and artificial intelligence have formed the backbone for cryptocurrency portfolio optimization. Researchers have investigated various state of the art machine learning models to predict Bitcoin price and volatility. Machine learning models like recurrent neural network (RNN) and long short-term memory (LSTM) have been shown to perform better than traditional time series models in cryptocurrency price prediction. However, very few studies have applied sequence models with robust feature engineering to predict future pricing. In this study, we investigate a framework with a set of advanced machine learning forecasting methods with a fixed set of exogenous and endogenous factors to predict daily Bitcoin prices. We study and compare different approaches using the root mean squared error (RMSE). Experimental results show that the gated recurring unit (GRU) model with recurrent dropout performs better than popular existing models. We also show that simple trading strategies, when implemented with our proposed GRU model and with proper learning, can lead to financial gain.

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Section: Model Implementation and Resultsmentioning

confidence: 99%

A Gated Recurrent Unit Approach to Bitcoin Price Prediction

Dutta¹,

Kumar

Basu³

2019

SSRN Journal

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“…Their results suggest that the ANN network outperforms the Trend Follower meaning that the model is able to evaluate valuable information. [15]predict Cryptocurrency prices by using Linear Regression (LR) and Support Vector Machine (SVM) for the period of 2012-2018. Their sample consists of daily data.…”

Section: Prediction Methodologiesmentioning

confidence: 99%

A Survey on Anticipation the Prices of crypto currency using Deep Learning

Akshaya¹

2019

IJRASET

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Cryptocurrency is a decentralized digital currency, it without a central bank or single administrator that can be sent from user-to-user on the peer-to-peer network without the need for the negotiator. Cryptocurrency has been one of the top hit in social media and search engines recently. Their high volatility leads to the great potential of high profit if intelligent inventing strategies are taken. Unfortunately, due to their lack of indexes, Cryptocurrencies are relatively volatile compared to traditional fiat currencies. Hence when wisely invested with the assistance of predicted data, increased profit is achievable.

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“…The researchers discovered that the generalized regression neural networks were not as successful as LSTM in finding patterns in addition to being time-consuming. Based on reference [28], the Bitcoin closing price was predicted by using ML methods, LR, L-SVM, and P-SVM. Bitcoin's closing price was predicted for MA and WMA filters by using daily closing price, highest price, and lowest price time series.…”

Section: Machine Learning-machine Learningmentioning

confidence: 99%

Review on Bitcoin Price Prediction Using Machine Learning and Statistical Methods

Kervanci

Akay

2020

Sakarya University Journal of Computer and Information Sciences

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Bitcoin is invented in 2009 by the pseudonymous Satoshi Nakamoto. Bitcoin is a decentralized digital currency system [1]. Bitcoin is the most acknowledged cryptocurrency in the world, which provide it interesting for financier. The cryptocurrency market capitalization on date 22nd July 2020 value represents roughly USD 277 billion of dollars, bitcoin representing 62% of it. However, a disadvantage for investors is the difficulty of predicting the price of bitcoin due to the high volatility of the bitcoin exchange rate. Measurement, estimation, and modeling of currency exchange rate volatility compose a significant research area. For this reason, a lot of studies done about bitcoin price prediction both Machine Learning (ML) and Statistical Methods. In comparison studies, ML methods perform better in general. This review is a comprehensive study on how we can better predict bitcoin prices by grouping previously done studies. The presentation of Bitcoin price prediction studies in groups reveals, the difference from other review studies. These are statistical methods, ML and statistical methods, ML-ML, frequency effect of selected time, effect of social media and web search engine, causality, optimization of hyperparameters methods.

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Prediction of Bitcoin prices with machine learning methods using time series data

Cited by 74 publications

References 9 publications

A Gated Recurrent Unit Approach to Bitcoin Price Prediction

A Gated Recurrent Unit Approach to Bitcoin Price Prediction

A Survey on Anticipation the Prices of crypto currency using Deep Learning

Review on Bitcoin Price Prediction Using Machine Learning and Statistical Methods

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