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We carry on a long term analysis for Bitcoin price, which is currently among the most renowned crypto assets available on markets other than Forex. In the last decade Bitcoin has been under spotlights among traders all world wide, both because of its nature of pseudo–currency and for the high volatility its price has frequently experienced. Considering that Bitcoin price has earned over five orders of magnitude since 2009, the interest of investors has been increasingly motivated by the necessity of accurately predicting its value, not to mention that a comparative analysis with other assets as silver and gold has been under investigation, too. This paper reports two approaches for a long term Bitcoin price prediction. The first one follows more standard paradigms from regression and least squares frameworks. Our main contribution in this regard fosters conclusions which are able to justify the cyclic performance of Bitcoin price, in terms of its Stock–to–Flow. Our second approach is definitely novel in the literature, and indicates guidelines for long term forecasts of Bitcoin price based on Machine Learning (ML) methods, with a specific reference to Support Vector Machines (SVMs). Both these approaches are inherently data–driven, and the second one does not require any of the assumptions typically needed by solvers for classic regression problems. [ABSTRACT FROM AUTHOR]

This study utilizes classification models to provide a robust algorithm for imbalanced data where the minority class is of the interest, that is, in the context of default payments. In developing an integrated predictive accuracy algorithm, this study proposes machine learning classifiers and applies DNN, SVM, KNN, and ANN. The proposed algorithm utilizes a 30,000 imbalanced dataset to improve the accuracy of the prediction of default payments by implementing oversampling and undersampling strategies, such as synthetic minority oversampling technique (SMOTE), SVM SMOTE, random undersampling, and ALL-KNN. The results indicate that the SVM under the ALL-KNN sampling technique is able to achieve an accuracy of 98.6%, with the lowest cross entropy loss measurement of 0.028. Through the accurate implementation of the neural networks and neurons used in the proposed algorithm, this paper presents better insights into the functioning of the neural networks when used in conjunction with the resampling techniques. Using the methodology and algorithm presented in this study, credit risk assessments can be more accurately predicted in practical applications where most of the clients are categorized as non-default payments. [ABSTRACT FROM AUTHOR]

Support vector machines (SVM) is one of the well known supervised classes of learning algorithms. Basic SVM models are dealing with the situation where the exact values of the data points are known. This paper studies SVM when the data points are uncertain. With some properties known for the distributions, chance-constrained SVM is used to ensure the small probability of misclassification for the uncertain data. As infinite number of distributions could have the known properties, the robust chance-constrained SVM requires efficient transformations of the chance constraints to make the problem solvable. In this paper, robust chance-constrained SVM with second-order moment information is studied and we obtain equivalent semidefinite programming and second order cone programming reformulations. The geometric interpretation is presented and numerical experiments are conducted. Three types of estimation errors for mean and covariance information are studied in this paper and the corresponding formulations and techniques to handle these types of errors are presented. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) is one of the most important class of machine learning models and algorithms, and has been successfully applied in various fields. Nonlinear optimization plays a crucial role in SVM methodology, both in defining the machine learning models and in designing convergent and efficient algorithms for large-scale training problems. In this paper we present the convex programming problems underlying SVM focusing on supervised binary classification. We analyze the most important and used optimization methods for SVM training problems, and we discuss how the properties of these problems can be incorporated in designing useful algorithms. [ABSTRACT FROM AUTHOR]

Twin support vector machine (TWSVM) and twin support vector regression (TSVR) are newly emerging efficient machine learning techniques which offer promising solutions for classification and regression challenges respectively. TWSVM is based upon the idea to identify two nonparallel hyperplanes which classify the data points to their respective classes. It requires to solve two small sized quadratic programming problems (QPPs) in lieu of solving single large size QPP in support vector machine (SVM) while TSVR is formulated on the lines of TWSVM and requires to solve two SVM kind problems. Although there has been good research progress on these techniques; there is limited literature on the comparison of different variants of TSVR. Thus, this review presents a rigorous analysis of recent research in TWSVM and TSVR simultaneously mentioning their limitations and advantages. To begin with, we first introduce the basic theory of support vector machine, TWSVM and then focus on the various improvements and applications of TWSVM, and then we introduce TSVR and its various enhancements. Finally, we suggest future research and development prospects. [ABSTRACT FROM AUTHOR]

Supervised learning methods are powerful techniques to learn a function from a given set of labeled data, the so-called training data. In this paper the support vector machines approach for regression is investigated under a theoretical point of view that makes use of convex analysis and Fenchel duality. Starting with the corresponding Tikhonov regularization problem, reformulated as a convex optimization problem, we introduce a conjugate dual problem to it and prove that, whenever strong duality holds, the function to be learned can be expressed via the optimal solutions of the dual problem. Corresponding dual problems are then derived for different loss functions. The theoretical results are applied by numerically solving the regression task for two data sets and the accuracy of the regression when choosing different loss functions is investigated. [ABSTRACT FROM AUTHOR]

We compare the performance of various advanced forecasting techniques, namely artificial neural networks, k-nearest neighbors, logistic regression, Naïve Bayes, random forest classifier, support vector machine, and extreme gradient boosting classifier to predict stock price movements based on past prices. We apply these methods with the high frequency data of 27 blue-chip stocks traded in the Istanbul Stock Exchange. Our findings reveal that among the selected methodologies, random forest and support vector machine are able to capture both future price directions and percentage changes at a satisfactory level. Moreover, consistent ranking of the methodologies across different time frequencies and train/test set partitions prove the robustness of our empirical findings. [ABSTRACT FROM AUTHOR]

Brain-Computer Interfaces (BCIs) are systems allowing people to interact with the environment bypassing the natural neuromuscular and hormonal outputs of the peripheral nervous system (PNS). These interfaces record a user's brain activity and translate it into control commands for external devices, thus providing the PNS with additional artificial outputs. In this framework, the BCIs based on the P300 Event-Related Potentials (ERP), which represent the electrical responses recorded from the brain after specific events or stimuli, have proven to be particularly successful and robust. The presence or the absence of a P300 evoked potential within the EEG features is determined through a classification algorithm. Linear classifiers such as stepwise linear discriminant analysis and support vector machine (SVM) are the most used discriminant algorithms for ERPs' classification. Due to the low signal-to-noise ratio of the EEG signals, multiple stimulation sequences (a.k.a. iterations) are carried out and then averaged before the signals being classified. However, while augmenting the number of iterations improves the Signal-to-Noise Ratio, it also slows down the process. In the early studies, the number of iterations was fixed (no stopping environment), but recently several early stopping strategies have been proposed in the literature to dynamically interrupt the stimulation sequence when a certain criterion is met in order to enhance the communication rate. In this work, we explore how to improve the classification performances in P300 based BCIs by combining optimization and machine learning. First, we propose a new decision function that aims at improving classification performances in terms of accuracy and Information Transfer Rate both in a no stopping and early stopping environment. Then, we propose a new SVM training problem that aims to facilitate the target-detection process. Our approach proves to be effective on several publicly available datasets. [ABSTRACT FROM AUTHOR]

Knowledge management is widely considered as a strategic tool to increase firm performance by enabling the reuse of organizational knowledge. Although many have studied knowledge management in a variety of business settings, the concept of tacit knowledge, especially the individual one, has not been explored in due detail. The objective of this study is to identify and prioritize individual tacit knowledge criteria and to explain their effects on firm performance. In the proposed methodology, first, the most prevalent individual tacit knowledge variables are identified by means of knowledge elicitation and feature selection methods. Then, the extracted variables were prioritized using machine learning methods and fuzzy Analytic Hierarchy Process (AHP). Support vector machine (SVM), logistic regression, and artificial neural networks are used as the first approach, followed by fuzzy AHP as the second approach. Based on the comparative analysis results, SVM (as the best-performed machine-learning technique) and fuzzy AHP methods were identified for the subsequent analysis. The results showed that both SVM and fuzzy AHP determined time efficiency of employees, communication between employees and supervisors, and innovative capability of employees as the most important tacit knowledge criteria. These findings are mostly supported by the extant literature, and collectively shows the synergistic nature of the utilized analytics approaches in determining individual tacit knowledge criteria. [ABSTRACT FROM AUTHOR]

When to declare the third innings in a test cricket match is a crucial decision directly impacting the outcome of the match. The captain of the side batting in the third innings takes into account factors like lead runs, batting strength of the opposition, favorability of the pitch, approximate number of overs left in the game, etc. to make the decision. The objective of this study is to develop a decision support system for the captain using machine learning algorithms to predict the outcome of a test match at different stages of the match. This will aid the captain to decide when to declare. Several new crucial factors are identified that affect the match outcome. Declaration decisions of past test matches are analyzed using probability functions of win, loss, and draw derived using these models. Previous researches have used only simple regression based techniques to predict the match outcome with low accuracy. Data of 354 test matches from 2008 to 2017 has been used to train and test the algorithms. Support vector machine is found to be the most accurate with an accuracy of 88.8%. [ABSTRACT FROM AUTHOR]

In this study, the predictability of the most liquid twelve cryptocurrencies are analyzed at the daily and minute level frequencies using the machine learning classification algorithms including the support vector machines, logistic regression, artificial neural networks, and random forests with the past price information and technical indicators as model features. The average classification accuracy of four algorithms are consistently all above the 50% threshold for all cryptocurrencies and for all the timescales showing that there exists predictability of trends in prices to a certain degree in the cryptocurrency markets. Machine learning classification algorithms reach about 55–65% predictive accuracy on average at the daily or minute level frequencies, while the support vector machines demonstrate the best and consistent results in terms of predictive accuracy compared to the logistic regression, artificial neural networks and random forest classification algorithms. [ABSTRACT FROM AUTHOR]

Kernel-based methods (KBMs) such as support vector machines (SVMs) are popular data mining tools for solving classification and regression problems. Due to their high prediction accuracy, KBMs have been successfully used in various fields. However, KBMs have three major drawbacks. First, it is not easy to obtain an explicit description of the discrimination (or regression) function in the original input space and to make a variable selection decision in the input space. Second, depending on the magnitude and numeric range of the given data points, the resulting kernel matrices may be ill-conditioned, with the possibility that the learning algorithms will suffer from numerical instability. Although data scaling can generally be applied to deal with this problem and related issues, it may not always be effective. Third, the selection of an appropriate kernel type and its parameters can be a complex undertaking, with the choice greatly affecting the performance of the resulting functions. To overcome these drawbacks, we present here the sparse signomial classification and regression (SSCR) model. SSCR seeks a sparse signomial function by solving a linear program to minimize the weighted sum of the ℓ-norm of the coefficient vector of the function and the ℓ-norm of violation (or loss) caused by the function. SSCR employs the signomial function in the original variables and can therefore explore the nonlinearity in the data. SSCR is also less sensitive to numerical values or numeric ranges of the given data and gives a sparse explicit description of the resulting function in the original input space, which will be useful for the interpretation purpose in terms of which original input variables and/or interaction terms are more meaningful than others. We also present column generation techniques to select important signomial terms in the classification and regression processes and explore a number of theoretical properties of the proposed formulation. Computational studies demonstrate that SSCR is at the very least competitive and can even perform better compared to other widely used learning methods for classification and regression. [ABSTRACT FROM AUTHOR]