Deep Learning Based Bitcoin Price Forecasing Using LSTM

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International Journal for Research in Applied Science & Engineering Technology (IJRASET)

J Kumara Swamy1, Navya V K2

1 (Asst. Prof.) Dept of Computer Science and Engineering, MVJ College Of Engineering Bengaluru, Karnataka, India.

Abstract: Bitcoin is one of the most popular and valuable cryptocurrency in the current financial market, attracting traders for investment and thereby opening new research opportunities for researchers. Countless research works have been performed on Bitcoin price prediction with different machine learning prediction algorithms. For the research: relevant features are taken from the dataset having strong correlation with Bitcoin prices and random data chunks are then selected to train and test the model.

The random data which has been selected for model training, may cause unfitting outcomes thus reducing the price prediction accuracy.

Here, a proper method to train a prediction model is being scrutinised. The proposed methodology is then applied to train a simple Long Short Term Memory (LSTM) model to predict the bitcoin price for the upcoming 30 days. When the LSTM model is trained with a suitable data chunk, thus identified, sustainable results are found for the prediction. In the end of this paper, the work culminates with future improvements.

Keywords: Bitcoin, Cryptocurrency, Machine Learning, Price Prediction, Deep Learning, LSTM (Long Short Term Memory)

I. INTRODUCTION

Instead of any direct human investments, generating profit with the help of algorithms is a common practice in the stock market. Many case studies have been performed to reach the conclusion that mathematical models warrant better results than humans. Bitcoins are an eye catching initiative in the fields of cryptography, economics, and computer sciences, as such currencies have a special character which is gained when integrating currency units with cryptographic technology. Due to the fact that cryptocurrency has a minute history, when compared to the stock market, new and unexplored territories are thus being scouted. Structurally, both the stock market and the cryptocurrency price data are having characteristics such as time series data, but high volatility is routinely present in the latter, with heavy wavering in the prices. A cryptocurrency market differs from a traditional stock market in the respect that the former has a lot of new features. It is required to apply new techniques for prediction suitable for the cryptocurrency market. Fewer studies have been conducted on cryptocurrency price prediction when compared to the stock market. In this paper, we are predicting the Bitcoin price trend using a Long Short-Term Memory (LSTM) model.

Our model is aimed to predict the next thirty days price of Bitcoin.To develop a model which can help us to predict the price of the crypto currency used (in this case: Bitcoin), with low error rate and a high precision of accuracy. The model will not tell the future, but it might forecast the general trend and the direction to expect the prices to move.While using this model, first, the dataset of the crypto currency used needs to be uploaded.

This, usually, contains the various features that the prediction model has to depend on. For e.g. average block size, total number of Bitcoins mined, day high & day low (highest and lowest values of different days), number of transactions, trade volume, etc. Then, secondly, the dataset will be applied on the regression model to obtain the predicted price.What the model proposes to do is that, first the data on Bitcoin Price fluctuations is gathered, of the past couple of years, from the internet. Then, after the process of data acquisition, the database should be organised.

The database is divided into various spreadsheet files, which are then uploaded to the software mainly used for data processing. The necessary calculations, like classification and regression, are then done. And finally the results are evaluated in terms of accuracy, error rates involved.

II. LITERATURE SURVEY

The literature survey was carried out to find various papers published in international journals related to various Bitcoin price prediction algorithms, and associate the best algorithm for the same.

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