On the usage of support vector machines for the short-term price movement prediction in intra-day trading

The aim of the current thesis is to research the prediction of future stock prices by using the implementation of support vector machines, also to find possible technical solutions and to interpret the gained results. In order to consider the problem of forecasting future stock prices for a short period of time, the market data of the British multinational telecommunications company Vodafone Group Plc and the British-Swedish multinational pharmaceutical and biologics company AstraZeneca Plc is being used to fit the models and verify how good their predictive power is. The opportunities of packages e1071 and kernlab of programming language R are being used in the current thesis. The implementation of the predictions to trading algorithms is not being considered due to it is not relevant to the underlying thesis. The thesis consists of three chapters. The first chapter is dedicated to support vector machines, because this particular method is used in developing prediction algorithms. For better understanding of the principle of this method, certain fundamentals are being explained. The first chapter introduces what is machine learning, explains finding the regression function by using support vector machines and mentions the problems which may arise during finding the regression function. The concept of regression estimation is being explained with theoretical and graphical examples. The second chapter is dedicated to kernels, because that gives an opportunity to use non-linear functions as regression functions. In this chapter, the classification of kernels is being introduced. In addition, it is explained to the reader why does the usage of kernel functions simplify the finding of the regression function. The short overview of technical opportunities of programming language R packages is also being introduced in the second chapter. Finally, such statistical method of evaluating and comparing learning algorithms as cross-validation is being briefly mentioned in the chapter. Unlike from the first two chapters, which give a theoretical overview, the third chapter is the practical part of the thesis. It introduces the implementation of support vector machines on the short-term price movement prediction in intra-day trading. The algorithm of the price prediction is being explained in the third chapter. Given data is also described in this chapter. Due to similar data involved, the author also presents the comparison with the master’s thesis of Andrei Orlov [1]. In addition, at the end of the thesis, the reader can find Appendices which consist of data frame, the diagram explaining the relations between functions in a code of algorithm, the codes of figures and the CD containing the code of the algorithm

The efficacy of using data mining techniques in predicting academic performance of architecture students.

In recent years, there has been a tremendous increase in the number of applicants seeking placement in the undergraduate architecture programme. It is important to identify new intakes who possess the capability to succeed during the selection phase of admission at universities. Admission variable (i.e. prior academic achievement) is one of the most important criteria considered during selection process. The present study investigates the efficacy of using data mining techniques to predict academic performance of architecture student based on information contained in prior academic achievement. The input variables, i.e. prior academic achievement, were extracted from students' academic records. Logistic regression and support vector machine (SVM) are the data mining techniques adopted in this study. The collected data was divided into two parts. The first part was used for training the model, while the other part was used to evaluate the predictive accuracy of the developed models. The results revealed that SVM model outperformed the logistic regression model in terms of accuracy. Taken together, it is evident that prior academic achievement are good predictors of academic performance of architecture students. Although the factors affecting academic performance of students are numerous, the present study focuses on the effect of prior academic achievement on academic performance of architecture students. The developed SVM model can be used a decision-making tool for selecting new intakes into the architecture program at Nigerian universities

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie