Bitcoin Price Prediction Using Machine Learning and Artificial Neural Network Model

Abstract

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.

Bitcoin Price Prediction: ARIMA & SARIMA vs Linear Regression

“…Although this study shows a high-level predictive performance, the data set is relatively small for testing a model to run under the real circumstances. From a paper published in Indian Journal of Science and Technology in 2021, the method applied was simple linear regression and achieved a high accuracy which is 99.97% in both training and test set [11].…”

System for Analysis and Prediction of Trends in Cryptocurrency Market

“…A very few papers focused on Bitcoin price prediction. Recently, many authors proposed [5,6,7] machine learning algorithms to predict Bitcoin price. [8] Authors used Fast Wavelet Transform to check the changes in Bitcoin prices.…”

“…An ARIMA model is a class of statistical models for analyzing and forecasting time series data A pure Auto Regressive (AR only) model is one where Yt depends only on its own lags. That is, Yt is a function of the 'lags of Yt' (6) where, Yt+1 is the lag 1 of the series, βt is the coefficient of lag 1 that the model estimates and α is the intercept term, also estimated by the model. Likewise, a pure Moving Average (MA only) model is one where Yt depends only on the lagged forecast errors.…”

Height-end Multi-layer Perceptron and Machine Learning Methods of Forecasting Bitcoin Price Time Series

“…This resulted in 99% prediction accuracy with specific features of network settings, but could not perform well with other crypto-currencies like Litecoin, Etherium and Ripple, etc. Ho et al, [42] used the SLTM neural network with linear regression technique to predict the investment values of Bitcoin's fetaures. The Graphic user interface was created to help user read in the 4-input features(open, high, low and close prices) and predict the next target value of bitcoin.…”