03858ntm a22005177i 4500 000551456 CZ-PrVSE 20190203085345.0 m d cr n|||||||||| 190203s2018 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Leová, Zuzana ISIS:99420 dis Outliery a štrukturálne zmeny v časových radách s použitým state space modelov a indikátorovo-saturánym prístupom eng Outliers and structural breaks in time series using state space models and indicator saturation approach / Zuzana Leová 2018 ?? stran : digital, PDF soubor Vedoucí práce: Jan Zouhar Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2019 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2019 With the rapid growth of available data, many companies are struggling to identify valuable trends and important changes in the data structure. Unexpected changes in unobserved components of time series, such as shocks or trend changes, can significantly impact the choice of the best fitting model, diagnostic tests, and forecast creation. To create valuable insights, the automatic detection of outliers and structural changes is more often necessary than merely desired. Kalman filteringand smoothing techniques have proved to be both precise and flexible in extracting unobserved components in time series. With incorporation of the indicator saturation approach and step-wise variable selection, we develop a technique to effectively identify outliers and changes in structural time series, including detection of the particular type of outlier or change. Considering the basic structural model, we focus on the detection of four types: additive outliers, level shifts, trendchanges, and seasonal additive outliers. We use Monte Carlo simulations to investigate the performance of the algorithms under different variances, outlier magnitudes, and locations. Next, wecompare the simulation results with the performance on a real data set with eight time series of hierarchical structure, in comparison to alternative outlier detection techniques. We found that thedeveloped method is able to capture the time series structure without over-fitting; therefore, the detection procedure is more precise. On the other hand, in comparison to the alternative techniques,our approach is computationally more expensive. Thus it should be used carefully with respect to specific use cases. Způsob přístupu: Internet ekonometrie a operační výzkum [obor dipl. práce] diplomové práce fd132022 czenas master's theses eczenas Basic structural model State Space models Time series econometrics Indicator saturation approach Zouhar, Jan, 1982- vse2009539908 ths Holý, Vladimír ISIS:116093 opn Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky kn20010709399 dgg https://insis.vse.cz/zp/62267/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/62267 Hlavní práce https://insis.vse.cz/zp/62267/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/62267/posudek/oponent/60091 Oponentura https://insis.vse.cz/zp/62267/priloha/17847 Přiloha k práci https://insis.vse.cz/zp/62267/podrobnosti dc:identifier NEPOSILAT VSKP vse62267 190201 62267