Forecasting Cryptocurrency Price Movement Using Moving Average Method : A Case Study of Bitcoin Cash

Bakar, Nashirah Abu; Rosbi, Sofian; Uzaki, Kiyotaka

doi:10.21474/ijar01/10188

Cited by 9 publications

(2 citation statements)

References 5 publications

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“…The proposed models worked better than other models in predicting the changes in the price of bitcoin. Abu Bakar, Rosbi, and Uzaki (2019) used a moving‐average method to predict the bitcoin price. Data collected from October 1 until December 20, 2019, was used in experiments.…”

Section: Traditional Statistical Techniques For Cryptocurrency Price mentioning

confidence: 99%

Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey

Khedr

Arif

et al. 2021

Intell Sys Acc Fin Mgmt

106

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Cryptocurrencies are decentralized electronic counterparts of government-issued money. The first and best-known cryptocurrency example is bitcoin. Cryptocurrencies are used to make transactions anonymously and securely over the internet. The decentralization behavior of a cryptocurrency has radically reduced central control over them, thereby influencing international trade and relations. Wide fluctuations in cryptocurrency prices motivate the urgent requirement for an accurate model to predict its price. Cryptocurrency price prediction is one of the trending areas among researchers. Research work in this field uses traditional statistical and machine-learning techniques, such as Bayesian regression, logistic regression, linear regression, support vector machine, artificial neural network, deep learning, and reinforcement learning. No seasonal effects exist in cryptocurrency, making it hard to predict using a statistical approach. Traditional statistical methods, although simple to implement and interpret, require a lot of statistical assumptions that could be unrealistic, leaving machine learning as the best technology in this field, being capable of predicting price based on experience. This article provides a comprehensive summary of the previous studies in the field of cryptocurrency price prediction from 2010 to 2020. The discussion presented in this article will help researchers to fill the gap in existing studies and gain more future insight.

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Section: Traditional Statistical Techniques For Cryptocurrency Price mentioning

confidence: 99%

Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey

Khedr

Arif

et al. 2021

Intell Sys Acc Fin Mgmt

106

View full text Add to dashboard Cite

show abstract

“…Therefore, for signals in the time domain, signal prediction methods based on statistics are more suitable. For signal forecasting, the classical statistical methods are based on the moving average method, which is easily disturbed by noise [43]. Methods developed on the basis of classical methods, such as X11, SEATS (Signal extraction in ARIMA time series) [44], and STL [45], can achieve more robust results.…”

Section: Introductionmentioning

confidence: 99%

Online Yarn Breakage Detection: A Reflection-Based Anomaly Detection Method

Yan

Zhu

Yang

et al. 2021

IEEE Trans. Instrum. Meas.

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This paper discusses an optical reflection-based method for equipment anomaly detection that enhances weak signals and has high sensitivity to abnormalities. Automatic warp knitting machine yarn breakage detection, which has become an acknowledged difficulty in the textile field, is achieved. To the best of our knowledge, this is the first time that visual inspection has been applied to yarn breakage detection in weaving. Furthermore, based on the periodicity of the yarn distribution and the periodic motion law of the machine, the combined wavelet and STL (Seasonal and Trend decomposition using LOESS) decomposition method is proposed for yarn breakage detection. Finally, the efficiency and accuracy of the proposed method are verified experimentally. Our research is a successful application of reflection characteristics to the anomaly detection of non-Lambertian objects, which has implications for the high-precision anomaly detection of precision equipment.

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Hybrid Models for Predicting Cryptocurrency Price Using Financial and Non-Financial Indicators

Shrivastava,

Suleiman,

Alibasa

2023

Communications in Computer and Information Science

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Forecasting Cryptocurrency Price Movement Using Moving Average Method : A Case Study of Bitcoin Cash

Cited by 9 publications

References 5 publications

Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey

Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey

Online Yarn Breakage Detection: A Reflection-Based Anomaly Detection Method

Hybrid Models for Predicting Cryptocurrency Price Using Financial and Non-Financial Indicators

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