|
|
Statistical Approach for BTT probes distribution
Mekhalfia, Mohammed Lamine ; Procházka, Pavel ; Šmíd, R. ; Tchawou Tchuisseu, Eder Batista
Blade tip timing (BTT) system is a non-invasive method used for measuring the blade tip timing of rotating machinery. The BTT system typically consists of sensor probes, signal conditioning system, and data acquisition unit. The sensor probes are placed in proximity to the rotating blade tips, Sensors measure the time at which each blade passes by the probe. By measuring the blade tip timing, the BTT system can detect changes in the blade tip vibration, which is an important for the health monitoring and performance optimization of rotating machinery [1]. To ensure accurate measurement of blade tip timing, the probe arrangement is crucial. The placement of the probe relative to the blade tips can affect the measurement accuracy, and thus, several arrangement algorithms have been developed to optimize the placement of the probes. Instead of considering the condition number, a statistical algorithms is followed to optimize the placement of probes for accurate measurement of blade tip timing in turbo-machinery. The statistical approach will be employed to determine the optimal locations for probes, with the aim of reducing errors in the measurement of blade tip timing. \n
|
|
|
Pre-election surveys for the elections to the Chamber of Deputies in the years 1996-2021 and their success
Ilková, Veronika ; Stauber, Jakub (advisor) ; Charvát, Jan (referee)
The bachelor's thesis processes the data of the results of pre-election surveys for the elections to the Chamber of Deputies of the Parliament of the Czech Republic. The aim of the bachelor's thesis is to evaluate the quality of pre-election polls for the period from the first elections to the Chamber of Deputies in 1996 to the last elections in 2021. At the same time, individual aspects of the surveys, which can affect their (poor) quality, are examined. The theoretical part of the thesis summarizes the historical development of the researched phenomenon. Subsequently, the basic methodological methods used in the construction of this type of surveys are presented. The individual types of surveys with electoral topis from the point of view of the contracting authority and the user are also described. The practical part defines the data used together with the basic introduction of agencies organizing pre-election surveys, it deals also with the chosen methodology. Selected statistical methods allow the processing of selected data and the subsequent answering of the main research question and two sub-questions. The thesis also provides closer view on ČSSD (Czech social democratic party) as the party whose prediction often significantly differed from the actual electoral gains.
|
|
|
Does LSTM neural network improve factor models' predictions of the European stock market?
Zelenka, Jiří ; Baruník, Jozef (advisor) ; Čech, František (referee)
This thesis wants to explore the forecasting potential of the multi-factor models to predict excess returns of the aggregated portfolio of the European stock mar- ket. These factors provided by Fama and French and Carhart are well-known in the field of asset pricing, we also add several financial and macroeconomic factors according to the literature. We establish a benchmark model of ARIMA and we compare the forecasting errors of OLS and the LSTM neural networks. Both models take the lagged excess returns and the inputs. We measure the performance with the root mean square error and mean absolute error. The results suggest that neural networks are in this particular task capable of bet- ter predictions given the same input as OLS but their forecasting error is not significantly lower according to the Diebold-Mariano test. JEL Classification C45, C53, C61, E37, G11, G15 Keywords Stocks, European market, Neural networks, LSTM, Factor Models, Fama-French, Predic- tions, RMSE Title Does LSTM neural network improve factor mod- els' predictions of the European stock market?
|
guest ::
