The aim of this diploma thesis is to analyze the development of the prices of real estate in the Czech Republic. The thesis is divided into three main parts. The first one deals with the theoretical introduction to valuation of the real estate. Moreover, the thesis presents the current development of the prices of real estate on the Czech market. The last part focuses on co-integration analysis, within which an ADL model is created. This model serves as a base for an error correction model, which describes short-term as well as long-term relations within the time series. The explanatory variables are gross domestic product, consumer price index, the amount of finished apartments, interest rate of mortgage loans, common rate of unemployment, and average gross monthly income. It is the one-equation model which describes the relation among the already mentioned explanatory variables and the HPI index. analysis of the development of the prices of real estate

Chemical sensor based systems that are used for detection, identification, or quantification of various gases are very complex in nature. Sensor response data collected as a multivariate time series signals encounters gradual change of the sensor characteristics(known as sensor drift) due to several reasons. In this thesis, drift component of a silicon carbide Field-Effect Transistor (SiC-FET) sensor data was analyzed using time series. The data was collected from an experiment measuring output response of the sensor with respect to gases emitted by certain experimental object at different temperatures. Augmented Dickey Fuller Test (ADF) was carried out to analyze the sensor drift which revealed that stochastic trend along with deterministic trend characterized the drift components of the sensor. The drift started to rise in daily measurements which contributed to the total drift. / Traditional Autoregressive Integrated Moving Average (ARIMA) and deep learning based Long Short-Term Memory (LSTM) algorithm were carried out to forecast the sensor drift in reduced set of data. However, reduction of the data size degraded the forecasting accuracy and imposed loss of information. Therefore, careful selection of data using only one temperature from the temperature cycle was chosen instead of all time points. This chosen data from sensor array outperformed forecasting of sensor drift than reduced dataset using both traditional and deep learning methods.