{"id":{"repo_id":"brno-tech","oai_identifier":"oai:dspace.vut.cz:11012/180581"},"canonical_url":"https://search.dev.ndltd.org/etd/brno-tech/oai:dspace.vut.cz:11012/180581","repository":{"repo_id":"brno-tech","name":"Brno University of Technology","base_url":"https://dspace.vut.cz/oai/request"},"display":{"title":"Detekce anomálií v množství generovaných záznamů o incidentech","abstract":"Táto bakalárska práca sa zaoberá problematikou detekcie anomálií v časových radoch. Predstavuje metódy STL decomposition, ARIMA, Exponential Smoothing a LSTM Networks. Cieľom je pomocou týchto metód vytvoriť algoritmus, ktorý dokáže analyzovať trend v množstve generovaných záznamov o incidentoch a detekovať anomálie z trendu. Riešenie bolo vytvorené na základe dátovej sady poskytnutej firmou AT&T Global Network Services Czech Republic s.r.o. a implementované v programovacom jazyku Python.","abstract_html":"Táto bakalárska práca sa zaoberá problematikou detekcie anomálií v časových radoch. Predstavuje metódy STL decomposition, ARIMA, Exponential Smoothing a LSTM Networks. Cieľom je pomocou týchto metód vytvoriť algoritmus, ktorý dokáže analyzovať trend v množstve generovaných záznamov o incidentoch a detekovať anomálie z trendu. Riešenie bolo vytvorené na základe dátovej sady poskytnutej firmou AT&T Global Network Services Czech Republic s.r.o. a implementované v programovacom jazyku Python.","abstract_has_math":false,"creators":["Šurina, Timotej"],"institution":"Vysoké učení technické v Brně. 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Predstavuje metódy STL decomposition, ARIMA, Exponential Smoothing a LSTM Networks. Cieľom je pomocou týchto metód vytvoriť algoritmus, ktorý dokáže analyzovať trend v množstve generovaných záznamov o incidentoch a detekovať anomálie z trendu. Riešenie bolo vytvorené na základe dátovej sady poskytnutej firmou AT&T Global Network Services Czech Republic s.r.o. a implementované v programovacom jazyku Python.","This bachelor thesis deals with the issue of time series anomaly detection. It presents methods STL decomposition, ARIMA, Exponential Smoothing and LSTM Networks. The aim is to use these methods to create an algorithm that can analyze the trend in a volume of generated incident tickets and detect anomalies form the trend. The solution was created based on a dataset provided by firm AT&T Global Network Services Czech Republic s.r.o. and implemented in the Python programming language."]},{"key":"dc:title","label":"Title","values":["Detekce anomálií v množství generovaných záznamů o incidentech"]}]}],"canonical_facts":{"dc:contributor.advisor":["Trchalík, Roman"],"dc:creator":["Šurina, Timotej"],"dc:description.abstract":["Táto bakalárska práca sa zaoberá problematikou detekcie anomálií v časových radoch. Predstavuje metódy STL decomposition, ARIMA, Exponential Smoothing a LSTM Networks. Cieľom je pomocou týchto metód vytvoriť algoritmus, ktorý dokáže analyzovať trend v množstve generovaných záznamov o incidentoch a detekovať anomálie z trendu. Riešenie bolo vytvorené na základe dátovej sady poskytnutej firmou AT&T Global Network Services Czech Republic s.r.o. a implementované v programovacom jazyku Python.","This bachelor thesis deals with the issue of time series anomaly detection. It presents methods STL decomposition, ARIMA, Exponential Smoothing and LSTM Networks. The aim is to use these methods to create an algorithm that can analyze the trend in a volume of generated incident tickets and detect anomalies form the trend. The solution was created based on a dataset provided by firm AT&T Global Network Services Czech Republic s.r.o. and implemented in the Python programming language."],"dc:identifier.other":["121890"],"dc:identifier.uri":["http://hdl.handle.net/11012/180581"],"dc:language.iso":["en"],"dc:publisher":["Vysoké učení technické v Brně. 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