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Statistical analysis of big industrial data
Zamazal, Petr ; Popela, Pavel (referee) ; Šomplák, Radovan (advisor)
This thesis deals with processing of real data regarding waste collection. It describes select parts of the fields of statistical tests, identification of outliers, correlation analysis and linear regression. This theoretical basis is applied through the programming language Python to process the data into a form suitable for creating linear models. Final models explain between 70 \% and 85 \% variability. Finally, the information obtained through this analysis is used to specify recommendations for the waste management company.
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Quality of The Track Geometry Progress
Nejezchlebová, Jitka ; Kulich, Pavel (referee) ; Svoboda, Richard (advisor)
The bachelor ’s thesis deals with the development of track geometry over time. The theoretical part describes the methodology for measuring and evaluating of track geometry. The practical part deals with the section evaluation on the double-track line Brno Maloměřice - Adamov. The MATLAB system was used for evaluation. For chosen sections, the graphs were regressed to determine the quality development for all parameters. Regressions were also used to determine which parameter deteriorates the fastest over time, in which track the parameters deteriorate faster and whether it can be said that the increase is faster before or after the repair.
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The spatial analysis of the 2018 Czech presidential election results in the capital city of Prague
Vratný, Radek ; Lepič, Martin (advisor) ; Šimon, Martin (referee)
The 2018 Czech presidential election were profoundly controversial and polarising event. One of the polarising factors was a perception that the voting behaviour in the capital city of Prague is significantly different compared to the voting behaviour in the rest of Czechia. Therefore, the primary objective of this thesis is to examine the electoral support of both runoff voting candidates across the territory of the capital city of Prague. The main part of the analysis focuses on the spatial differentiation of the election results of both candidates at the level of Prague's polling districts and on searching for the spatial clusters of electoral support. For the purpose of the study the methods of the exploratory spatial data analysis were used, specifically the global and local statistics of spatial autocorrelation. Subsequently, the thesis attempts to clarify which factors determined the voting behaviour through testing the socio-economic and demographic indicators using the method of multinomial linear regression. The analysis proved that the spatial differentiation of the election results of both candidates is significant across the territory of Prague and detected the spatial clusters of electoral support. The analysis also clarified that this voting behaviour can be explained particularly by...
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Business Intelligence - Use of Data Mining in Business Processes
Skalický, Tomáš ; Veselý, Martin (referee) ; Kříž, Jiří (advisor)
The aim of this bachelor thesis is to get acquainted with the concept of Business Intelligence as well as with the concept of data mining and its use in the company sphere. In the introductory theoretical part I will introduce the tools of Business Intelligence and data mining methods. In the following practical part I will use the methods for analysis of provided company data. The analysis obtained can be used as a support for company decision making.
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Genetic Programming in Prediction Tasks
Machač, Michal ; Mrázek, Vojtěch (referee) ; Sekanina, Lukáš (advisor)
This thesis introduces various machine learning algorithms which can be used in prediction tasks based on regression. Tree genetic programming and linear genetic programming are explained more thoroughly. Selected machine learning algorithms (linear regression, random forest, multilayer perceptron and tree genetic programming) are compared on publicly available datasets with the use of scikit-learn and gplearn libraries. A core part of this project is a new implementation of linear genetic programming which was developed in C++, tested on common symbolic regression problems and then evaluated on real datasets. Results obtained with the proposed system are compared with the results obtained with gplearn.
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Counting People Using a PIR Sensor
Beneš, Martin ; Kempter, Guido (referee) ; Drahanský, Martin (advisor)
PIR (pasivní infračervený) senzor se používá zejména pro detekci přítomnosti osoby a oznámení systému pro příslušnou reakci. Cílem této práce je užití PIR senzorů pro lokalizaci osoby a návrh způsobu pro určení počtu lidí ve snímaném prostoru. Pro tento účel je navržen způsob zpracování jeho výstupního analogového signálu, počínající extrakcí příznaků pomocí spojité vlnkové transformace, klasifikačního modelu založeném na fuzzy logice a lineární regresi. Na konci jsou představeny a vyhodnoceny experimentálně získané výsledky.
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Big Data Processing from Large IoT Networks
Benkő, Krisztián ; Podivínský, Jakub (referee) ; Krčma, Martin (advisor)
The goal of this diploma thesis is to design and develop a system for collecting, processing and storing data from large IoT networks. The developed system introduces a complex solution able to process data from various IoT networks using Apache Hadoop ecosystem. The data are real-time processed and stored in a NoSQL database, but the data are also stored in the file system for a potential later processing. The system is optimized and tested using data from IQRF network. The data stored in the NoSQL database are visualized and the system periodically generates derived predictions. Users are connected to this system via an information system, which is able to automatically generate notifications when monitored values are out of range.
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A Nonparametric Bootstrap Comparison of Variances of Robust Regression Estimators.
Kalina, Jan ; Tobišková, Nicole ; Tichavský, Jan
While various robust regression estimators are available for the standard linear regression model, performance comparisons of individual robust estimators over real or simulated datasets seem to be still lacking. In general, a reliable robust estimator of regression parameters should be consistent and at the same time should have a relatively small variability, i.e. the variances of individual regression parameters should be small. The aim of this paper is to compare the variability of S-estimators, MM-estimators, least trimmed squares, and least weighted squares estimators. While they all are consistent under general assumptions, the asymptotic covariance matrix of the least weighted squares remains infeasible, because the only available formula for its computation depends on the unknown random errors. Thus, we take resort to a nonparametric bootstrap comparison of variability of different robust regression estimators. It turns out that the best results are obtained either with MM-estimators, or with the least weighted squares with suitable weights. The latter estimator is especially recommendable for small sample sizes.
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How to down-weight observations in robust regression: A metalearning study
Kalina, Jan ; Pitra, Z.
Metalearning is becoming an increasingly important methodology for extracting knowledge from a data base of available training data sets to a new (independent) data set. The concept of metalearning is becoming popular in statistical learning and there is an increasing number of metalearning applications also in the analysis of economic data sets. Still, not much attention has been paid to its limitations and disadvantages. For this purpose, we use various linear regression estimators (including highly robust ones) over a set of 30 data sets with economic background and perform a metalearning study over them as well as over the same data sets after an artificial contamination.
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How to down-weight observations in robust regression: A metalearning study
Kalina, Jan ; Pitra, Zbyněk
Metalearning is becoming an increasingly important methodology for extracting knowledge from a data base of available training data sets to a new (independent) data set. The concept of metalearning is becoming popular in statistical learning and there is an increasing number of metalearning applications also in the analysis of economic data sets. Still, not much attention has been paid to its limitations and disadvantages. For this purpose, we use various linear regression estimators (including highly robust ones) over a set of 30 data sets with economic background and perform a metalearning study over them as well as over the same data sets after an artificial contamination. We focus on comparing the prediction performance of the least weighted squares estimator with various weighting schemes. A broader spectrum of classification methods is applied and a support vector machine turns out to yield the best results. While results of a leave-1-out cross validation are very different from results of autovalidation, we realize that metalearning is highly unstable and its results should be interpreted with care. We also focus on discussing all possible limitations of the metalearning methodology in general.
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