Forecasting the Price of the Cryptocurrency Using Linear and Nonlinear Error Correction Model

Kim, Jong Min; Cho, Chanho; Jun, Chulhee

doi:10.3390/jrfm15020074

Cited by 10 publications

(5 citation statements)

References 16 publications

(15 reference statements)

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“…The results of the comparative analysis show that RNNs provide the most promising results. Jong-Min, Cho and Jun [25] use linear and non-linear Error Correction Model (ECM) to forecast BTC daily returns. Linear ECM is the best BTC prediction model compared to neural network and autoregressive models in terms of RMSE, MAE and MAPE.…”

Section: Literature Reviewmentioning

confidence: 99%

Bitcoin monthly return forecast: A comparison of ARIMA and multi layer Perceptron Artificial Neural Network

Lazović,

Đorđević,

Lukić

2023

International Review

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In this paper, we compare the predictive power of Auto Regressive Integrated Moving Averages (ARIMA) and Multi-Layer Perceptron Artificial Neural Networks (MLP ANN) model to short-term forecast the monthly returns of Bitcoin cryptocurrency. We evaluate the performance of two models using time series with monthly data from January 2018 to December 2021. The key parameters for the final assessment of prognostic models are the values of Root Mean Square Error-RMSE and Forecast Error-FE. The results of the short-term BTC return forecast showed better properties of composite compared to univariate time series forecasting models, i.e., higher prognostic power of the MLP ANN model compared to the selected ARIMA (1,1,3) model (lower RMSE and FE). The results point to further comparative research of prognostic models and the possibility of forming more complex and hybrid structures of neural network models in order to predict economic phenomena as accurately as possible.

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Section: Literature Reviewmentioning

confidence: 99%

Bitcoin monthly return forecast: A comparison of ARIMA and multi layer Perceptron Artificial Neural Network

Lazović,

Đorđević,

Lukić

2023

International Review

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“…In 2021, R. K. Jana et al [14] proposed a regression framework based on differential evolution to predict bitcoin. They first decomposed the original sequence into granular linear and nonlinear components using maximum overlapping discrete wavelet transform, and then fitted polynomial regression with interaction (PRI) and support vector regression (SVR) on both linear and nonlinear components to obtain the componentwise projections.Apart from the previously introduced statistical methods, Jong-Min Kim et al [15] proposed to use linear and nonlinear error correction models to predict bitcoin log returns, and compared with neural network, ARIMA and other methods. The experiment was verified with the price data from 1 January 2019 to 27 August 2021.…”

Section: Related Workmentioning

confidence: 99%

A Stacking Ensemble Deep Learning Model for Bitcoin Price Prediction Using Twitter Comments on Bitcoin

Chen

et al. 2022

Mathematics

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Cryptocurrencies can be considered as mathematical money. As the most famous cryptocurrency, the Bitcoin price forecasting model is one of the popular mathematical models in financial technology because of its large price fluctuations and complexity. This paper proposes a novel ensemble deep learning model to predict Bitcoin’s next 30 min prices by using price data, technical indicators and sentiment indexes, which integrates two kinds of neural networks, long short-term memory (LSTM) and gate recurrent unit (GRU), with stacking ensemble technique to improve the accuracy of decision. Because of the real-time updates of comments on social media, this paper uses social media texts instead of news websites as the source data of public opinion. It is processed by linguistic statistical method to form the sentiment indexes. Meanwhile, as a financial market forecasting model, the model selects the technical indicators as input as well. Real data from September 2017 to January 2021 is used to train and evaluate the model. The experimental results show that the near-real time prediction has a better performance, with a mean absolute error (MAE) 88.74% better than the daily prediction. The purpose of this work is to explain our solution and show that the ensemble method has better performance and can better help investors in making the right investment decision than other traditional models.

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“…To forecast Bitcoin's log returns, Kim et al (2022) used linear and nonlinear error-correcting models (ECMs) (BTC). In terms of RMSE, MAE, and MAPE, the linear ECM outperforms the neural network and autoregressive models at predicting BTC.…”

Section: Literature Reviewmentioning

confidence: 99%

Arima Model for Forecasting the Bitcoin Exchange Rate Against the Usd

Vinayakamoorthi

Jeyarajah²,

Suresh³

et al. 2022

Int. J. Eng. Sci. Tech

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This study analysis forecasting the bitcoin exchange rate against the USD. The dataset selected for this study starts from January 2015 to June 2022. This study's methodology uses autoregressive integrated moving average forecasting (ARIMA). The overall outcomes of this study were gathered from the statistical software Minitab 21.1. The Box Jenkins approaches are also used to predict the best model. To determine the ARIMA model parameter, this study did autocorrelation function (ACF) and partial autocorrelation function (PACF) analyses. According to the Box-Cox transformation method, log transformation was selected. The outcome demonstrates that the seasonal with the regular difference in the Bitcoin exchange rate against the USD is a stationary data series. The forecasting model used in this study is ARIMA (1,1,0) (2,1,1)12. This predicted model is identified through the Mean squared error by comparing the other guessing ARIMA models. After the prediction, 5 Month bitcoin exchange rate against the USD. Investors will be able to estimate the bitcoin exchange rate against the USD with the use of this information, but volatility must also be properly watched. This will aid investors in making better investment decisions and increase profits. In future studies, better consider another exchange rate of BTC and software experts will develop such type of software based on ARIMA models for prediction.

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Forecasting the Price of the Cryptocurrency Using Linear and Nonlinear Error Correction Model

Cited by 10 publications

References 16 publications

Bitcoin monthly return forecast: A comparison of ARIMA and multi layer Perceptron Artificial Neural Network

Bitcoin monthly return forecast: A comparison of ARIMA and multi layer Perceptron Artificial Neural Network

A Stacking Ensemble Deep Learning Model for Bitcoin Price Prediction Using Twitter Comments on Bitcoin

Arima Model for Forecasting the Bitcoin Exchange Rate Against the Usd

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