CryptoAR: scrutinizing the trend and market of cryptocurrency using machine learning approach on time series data

Abstract

<span>Cryptocurrencies are encrypted digital or virtual money used to avoid counterfeiting and double spending. The scope of this study is to evaluate cryptocurrencies and forecast their price in the context of the currency rate trends. A public survey was conducted to determine which cryptocurrency is the most well-known among Bangladeshi people. According to the survey respondents, Bitcoin is the most famous cryptocurrency among the eight digital currencies. After that, we'll explore the four most well-known cryptocurrencies: Bitcoin, Ethereum, Litecoin, and Tether token. The 'YFinance' python package collects our cryptocurrency dataset, and the relative strength index (RSI) is employed to investigate these cryptocurrencies. Autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA) models are applied to our time-series data from 2015-1-1 to 2021-6-1. Using the 'closing' price and a simple moving average (SMA) graph, bitcoin and tether are identified as oversold or overbought cryptocurrencies. We employ the seasonal decomposed technique into the dataset before implementing the model, and the augmented dickey-fuller test (ADF) indicates too much seasonality in the dataset. The autoregressive (AR) model is the most accurate in predicting the price of Bitcoin, Ethereum, Litecoin, and Tether-token, with 97.21%, 96.04%, 95.8%, and 99.91% accuracy, consecutively.</span>

Bitcoin trading indicator: a machine learning driven real time bitcoin trading indicator for the crypto market

“…Before the arrangement takes place, data pre-handling is an essential task. For bitcoin, "yfinance" [20] is a very rich library that provides those data in a suitable style, so we do not need to fill in any kind of rows with a simple imputer. Since the bitcoin market is open around-the-clock, there is no risk of seasonal statistics being broken.…”

Tree-based Machine Learning and Deep Learning in Predicting Investor Intention to Public Private Partnership

“…Prior research that applied machine learning on prediction, classification and detection studies in financial, accounting and finance research highlight the effectiveness and accuracy of such methods to that of traditional statistical methods in problems such as in detection of financial fraud [8], firm performance [9] and finance [10], [11]. Despite the wiser used machine learning in accounting and finance, yet study on machine learning prediction and classification on PPP investment is limited.…”

Adaptation issues of machine learning in safety digitization

“…This interest could yield a torrent of unintellectual data utilizing amorphous stylistic communication commonly available to the public. The security investigators may be ethical white hat hackers assembling Internet communities to harmonize discovery schemes [3]- [5]. Here, in this scheme, specialists find vulnerabilities presented in the security applications and information to the manufacturer.…”