Next-Day Bitcoin Price Forecast

Munim, Ziaul Haque; Shakil, Mohammad Hassan; Alon, Ilan

doi:10.3390/jrfm12020103

Cited by 82 publications

(48 citation statements)

References 41 publications

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“…Based on the results of the research, the authors confirm the thesis that the indicators of online search and social networks really determine the dynamics of the rate of cryptocurrencies, therefore, this direction requires further research. Researchers [3,7,8,20,21] have proved that the complexity of the processes, the peculiarities of the formation time series of bitcoin and the frequency of requests for bitcoin require the use of the methodology of nonlinear dynamics, the methods of which are actively used in the study of processes during the manifestation of crisis phenomena [5,9], which in fully complies with the conditions of today. However, a fairly powerful toolkit of nonlinear dynamics only partially used in the study of the behavior of economy agents in the online information space, which proves the timeliness of this study.…”

Section: Related Workmentioning

confidence: 95%

Recurrence analysis of innovation behavior of bitcoin market agents in conditions of COVID-19

et al. 2021

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The relevance of the research subject is explained by a fundamental change in the conditions of existence and development of agents of the digital economy, limited knowledge about their behavior under conditions of quarantine restrictions. The aim of the research is to study the series of the dynamics of the price of bitcoin and the frequency of online requests for bitcoin as an indicator of the behavior of agents of the digital economy using the methods of qualitative recurrent analysis. The types of constructed time series plots of the price of bitcoin and the frequency of requests for bitcoin are defined as drift with a superimposed linearly gradually increasing sequence, which indicates the unpredictability of the behavior of digital economy agents with a gradual stabilization in new quality trend. The scientific novelty of the research results lies in the proven connection between the series of bitcoin price dynamics and the frequency of online requests for bitcoin, tracking changes in the behavior of digital economy agents before and after the introduction of quarantine restrictions. The procedure for conducting a qualitative recurrence analysis of the series of dynamics is generalized, which takes into account the specifics of the formation of the frequency of online requests for bitcoin, the price and the behavioral aspect of its formation. The practical value lies in defining the characterization of the behavior model of digital economy agents under conditions of quarantine restrictions. The behavior of digital economy agents in the context of COVID-2019 requires further research, in particular, using cross-recurrent analysis methods.

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Section: Related Workmentioning

confidence: 95%

Recurrence analysis of innovation behavior of bitcoin market agents in conditions of COVID-19

et al. 2021

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“… Indera et al (2018) presented a Multi-Layer Perceptron-based Non-Linear Autoregressive with Exogeneous Inputs (NARX) model to predict Bitcoin price starting from the opening, closing, minimum, maximum past prices and a technical indicator, the well-known Moving Average. Munim, Shakil & Alon (2019) forecasted Bitcoin price using the autoregressive integrated moving average (ARIMA) and neural network autoregression (NNAR) models. Uras et al (2020) segmented each analyzed financial time series into short partially overlapping sequences in such a way that these sequences do not resemble a random walk.…”

Section: Related Workmentioning

confidence: 99%

Predictions of bitcoin prices through machine learning based frameworks

Cocco¹,

Tonelli²,

Marchesi³

2021

PeerJ Computer Science

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The high volatility of an asset in financial markets is commonly seen as a negative factor. However short-term trades may entail high profits if traders open and close the correct positions. The high volatility of cryptocurrencies, and in particular of Bitcoin, is what made cryptocurrency trading so profitable in these last years. The main goal of this work is to compare several frameworks each other to predict the daily closing Bitcoin price, investigating those that provide the best performance, after a rigorous model selection by the so-called k-fold cross validation method. We evaluated the performance of one stage frameworks, based only on one machine learning technique, such as the Bayesian Neural Network, the Feed Forward and the Long Short Term Memory Neural Networks, and that of two stages frameworks formed by the neural networks just mentioned in cascade to Support Vector Regression. Results highlight higher performance of the two stages frameworks with respect to the correspondent one stage frameworks, but for the Bayesian Neural Network. The one stage framework based on Bayesian Neural Network has the highest performance and the order of magnitude of the mean absolute percentage error computed on the predicted price by this framework is in agreement with those reported in recent literature works.

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“…The proposed Lag-WMR model is compared with two benchmark methods, a typical time series prediction model ARIMA and a machine learning approach LGB model. ARIMA is a traditional time series forecasting model that has been widely applied in many fields of study such as finance [34], shipping [35], logistics [17], and electric power [36] and recently ARIMA model has been proved to be superior to the artificial neural networks in the short-term forecasting [37], [38]. And…”

Section: A Forecasting Comparisonmentioning

confidence: 99%

Short-Term Truckload Spot Rates’ Prediction in Consideration of Temporal and Between-Route Correlations

Xiao

Liu

et al. 2020

IEEE Access

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Truckload spot rate (TSR), defined as a price offered on the spot to transport a certain cargo by using an entire truck on a target transportation line, usually price per kilometer-ton, is a key factor in shaping the freight market. In particular, the prediction of short-term TSR is of great importance to the daily operations of the trucking industry. However, existing predictive practices have been limited largely by the availability of multilateral information, such as detailed intraday TSR information. Fortunately, the emerging online freight exchange (OFEX) platforms provide unique opportunities to access and fuse more data for probing the trucking industry. As such, this paper aims to leverage the high-resolution trucking data from an OFEX platform to forecast short-term TSR. Specifically, a lagged coefficient weighted matrixbased multiple linear regression modeling (Lag-WMR) is proposed, and exogenous variables are selected by the light gradient boosting (LGB) method. This model simultaneously incorporates the dependency between historical and current TSR (temporal correlation) and correlations between the rates on alternative routes (between-route correlation). In addition, the effects of incorporating temporal and between-route correlations, time-lagged correlation and exogenous variable selection in modeling are emphasized and assessed through a case study on short-term TSR in Southwest China. The comparative results show that the proposed Lag-WMR model outperforms autoregressive integrated moving average (ARIMA) model and LGB in terms of model fitting and the quality and stability of predictions. Further research could focus on rates' standardization, to define a practical freight index for the trucking industry. Although our results are specific to the Chinese trucking market, the method of analysis serves as a general model for similar international studies. INDEX TERMS Freight transportation, truckload spot rates, lagged weighted matrix, short-term prediction, weighted multiple regression, trucking economy.

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Next-Day Bitcoin Price Forecast

Cited by 82 publications

References 41 publications

Recurrence analysis of innovation behavior of bitcoin market agents in conditions of COVID-19

Recurrence analysis of innovation behavior of bitcoin market agents in conditions of COVID-19

Predictions of bitcoin prices through machine learning based frameworks

Short-Term Truckload Spot Rates’ Prediction in Consideration of Temporal and Between-Route Correlations

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