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Sensors 2021, 21, 6319 4 of 16 Algorithm 1 Approach to predict the daily classification of cryptocurrencies returns relating them to gold price and COVID-19 data using sensors. 1: UpdatetableofdeathsandinfectedcasedduetoCOVID-19withthedataextracted from the website https://github.com/CSSEGISandData/COVID-19 (accessed on 29 August 2021) and use this table for constructing the data warehouse. 2: Update table of gold prices with the data extracted from the website https://www. gold.org/goldhub/data/gold-prices (accessed on 29 August 2021) and employ this table for building the data warehouse. 3: Updatetableofcryptocurrencyreturnstakingthedailyclosingpriceandthemarket capitalization of the six studied digital currencies with the data extracted from the website https://coinmarketcap.com (accessed on 29 August 2021) and utilize this table for creating the data warehouse. 4: Sendawarningtothesoftwarethatperformsadataanalysiswhennewrecordsare detected by means of the trigger. Note that the database has a digital sensor with the ability to detect when new data are entered. 5: Communicatethetrigger(inthedatabase)withthesoftwarethroughamiddlewareso that the data analysis may be performed by executing the following steps: 5.1 Plot Figures 2–4, and construct Table 1. 5.2 Use the probability density function to state the type of skewness and kurtosis of the data empirical distribution and describe the shape of their distribution. 5.3 Employ the Jarque-Bera (JB) test and elaborate Table 2. 5.4 Execute the SVM algorithm considering the one-to-one method (see Section 3), that is, separate the observations into training and testing data sets, and select the best model based on the indicators defined in (11). 5.5 Apply the SVM algorithm considering the one-to-rest method (see Section 3) and build a dynamic model based on the available data. 5.6 Construct Figures 5–7. 6: Endthealgorithmreportingtheresultsoftheupdateddataanalysisinthegraphical user interface of the software. 3. Methodology SVM with binary classification is a powerful learning algorithm that aims to differen- tiate between two classes by finding an optimal hyperplane that maximizes the separation between the two-dimensional space points [23]. An algorithm that maximizes the margin between the training patterns and the deci- sion boundary is a helpful tool that can be applied to a wide variety of classifiers. In this tool, the number of parameters must be fit automatically to match the complexity of the problem, with the solution being formulated as a linear combination of support patterns, which are a subset of training patterns that are closest to the decision boundary. Next, we provide a background of the method used in the present investigation. For detailed mathematical derivations of the results presented in this section, we refer the readers to [34]. For m-dimensional space points, with 2 < m ∈ Z, we cannot utilize the binary SVM to obtain the separation hyperplane (decision surface) directly. To overcome this issue, the multi-classification problem breaks down into multiple binary classification problems. Then, the binary SVM algorithm is applied to these binary classes. This may be done in a one-to-one or one-to-rest manner explained as follows: • One-to-one method: Here, we break down the m classes into m(m − 1)/2 mutual binary classes, where a binary SVM is employed to differentiate between every twoPDF Image | Predicting Cryptocurrency Returns Based on the Gold Prices
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