Bitcoin Price Prediction and Analysis Using Deep Learning Models

Awoke, Temesgen; Rout, Minakhi; Mohanty, Lipika; Satapathy, Suresh Chandra

doi:10.1007/978-981-15-5397-4_63

Cited by 87 publications

(38 citation statements)

References 5 publications

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“…(7) The success of Bitcoin is measured by its huge capitalism growth and price, it leads to the emerging of various other crypto currencies which differ from Bitcoin in just a few parameters. (22) One of the primary reasons for people to dive into the crypto market is that it's very easy and simple to buy and sell assets via trading platforms such as WazirX, Binance, etc. These platforms are very easy to use and it does not take much time to create an account and start trading.…”

Section: Problem Statementmentioning

confidence: 99%

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Bitcoin Price Prediction Using Machine Learning and Artificial Neural Network Model

Ho¹,

Vatambeti²,

Ravichandran³

2021

IJST

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Objective: This paper explains the working of the linear regression and Long Short-Term Memory model in predicting the value of a Bitcoin. Due to its raising popularity, Bitcoin has become like an investment and works on the Block chain technology which also gave raise to other crypto currency. This makes it very difficult to predict its value and hence with the help of Machine Learning Algorithm and Artificial Neural Network Model this predictor is tested. Methodology: In this study, we have used data sets for Bitcoin for testing and training the ML and AI model. With the help of python libraries, the data filtration process was done. Python has provided with a best feature for data analysis and visualization. After the understanding of the data, we trim the data and use the features or attributes best suited for the model. Implementation of the model is done and the result is recorded. Finding: It was discovered that the linear regression model's accuracy rate is very high when compared to other Machine Learning models from related works; it was found to be 99.87 percent accurate. The LSTM model, on the other hand, shows a mini error rate of 0.08 percent. This, in turn, demonstrates that the neural network model is more optimized than the machine learning model. Novelty: In this work, a small GUI has been created using the tkinter library that will allow the user to input the High, Low, and Open features values and then predict the next value for the coin. This paper compares the prediction outcomes of a machine learning model and an artificial neural network model. Because linear regression provided the highest accuracy compared to the other machine learning models, we used it to compare it to the LSTM model.

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Section: Problem Statementmentioning

confidence: 99%

“…• Tkinter: To create a faster and quicker GUI application, with cooler features including the implementation of CSS support (13) . • Pickle: serialization and de-serialization of python object structure to store it in a file/database, maintaining program state, and transfer of data over a network (14,15) .…”

Section: Introductionmentioning

confidence: 99%

Bitcoin Price Prediction Using Machine Learning and Artificial Neural Network Model

Ho¹,

Vatambeti²,

Ravichandran³

2021

IJST

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“…Awoke et al [6] proposed bitcoin price prediction and analysis using deep learning models. They utilized the Long-Short-Term memory (LSTM) and the gated recurrent unit (GRU) to handle bitcoin volatility and predict its future price.…”

Section: Related Workmentioning

confidence: 99%

Predicting Foreign Exchange Using Digital Signal Processing

Robinson¹,

Kabari²

2021

British Journal of Computer, Networking and Information Technol

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The forex market is one associated with so much volatility and can lead to grave financial losses if not properly understood. To understand the market is to study the price patterns from previous years or months and make predictions from the rate of falling and rising. There have been so much researches aimed at developing a predictive model for the FOREX market, however, no model has been able to handle the market volatility while predicting future rates accurately. In this work, we have developed a digital processing model for predicting foreign exchange using ARIMA and Artificial Neural Network algorithms. We used price datasets for five currencies namely: USD, Swiss Pounds, Yen, Euro and Franc, gotten from the Central Bank of Nigeria (CBN) website. The data ranged from a period of 20 years. The model was simulated using MATLAB software. The study performed excellently in terms of time (26 seconds) and minimal errors (0.7). This work could be beneficial to FOREX traders and to the entire research community.

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“…While these models use different approaches and neural networks such as RNN [4], Bayesian neural networks (BNN) [30], LSTM and Gated Recurrent Units (GRU) [9], models such as [4] and [6], predict the future price of bitcoin using only attributes from historical market data. In [4], Open, High, Low, Close (OHLC) values were used through the implementation of a Bayesian optimized RNN and LSTM network.…”

Section: Introductionmentioning

confidence: 99%

Prediction of dogecoin price using deep learning and social media trends

Agarwal¹,

Harjule²,

Chouhan³

et al. 2021

EAI Endorsed Transactions on Industrial Networks and Intelligen

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INTRODUCTION: Cryptocurrency is a digital, decentralized form of money based on blockchain technology, which makes it the most secure method of making a transaction. There has been a huge increase in the number of cryptocurrencies in the past few years. Cryptocurrencies such as Bitcoin and Ethereum have become an interesting subject of study in fields such as finance. In 2021, over 4,000 cryptocurrencies are already listed. There are many past studies that focus on predicting the price of cryptocurrencies using machine learning, but the majority of them only focused on Bitcoin. Moreover, the majority of the models implemented for price prediction only used the historical market prices, and do not utilize social signals related to the cryptocurrency. OBJECTIVES: In this paper, we propose a deep learning model for predicting the prices of dogecoin cryptocurrency. The proposed model is based on historical market price data as well as social trends of Dogecoin cryptocurrency. METHODS:The market data of Dogecoin is collected from Kaggle on the granularity of a day and for the same duration the verified tweets have also been collected with hashtags "Dogecoin" and "Doge". Experimental results show that the proposed model yields a promising prediction of future price of Dogecoin, a cryptocurrency that has recently become the talk of the town of the crypto market. RESULTS: Minimum achieved RMSE in predicted price of Dogecoin was 0.02 where the feature vector consisted of OCVP (Open, Close, Volume, Polarity) values from combined dataset. RESULTS: Experimental results show that the proposed approach performs efficiently. s a major factor in the future price of Dogecoin.

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Bitcoin Price Prediction and Analysis Using Deep Learning Models

Cited by 87 publications

References 5 publications

Bitcoin Price Prediction Using Machine Learning and Artificial Neural Network Model

Bitcoin Price Prediction Using Machine Learning and Artificial Neural Network Model

Predicting Foreign Exchange Using Digital Signal Processing

Prediction of dogecoin price using deep learning and social media trends

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