04371ntm a22006257i 4500 000723138 CZ-PrVSE 20250608110309.0 m d cr n|||||||||| 250608s2025 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Šarapatková, Magdalena Marie ISIS:141014 dis Detekce bodových anomálií v rozsáhlých hierarchických časových řadách : Metody a aplikace eng Detection of Point-Wise Anomalies in Large-Scale Hierarchical Time Series : Methods and Applications Magdalena Marie Šarapatková 2025 ?? stran : digital, PDF soubor Vedoucí práce: Pavel Zimmermann Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2025 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2025 Anomaly detection in multivariate time series data is a critical challenge in various domains, including finance, healthcare, cyber security, retail, and many other fields. The ability to identify unexpected patterns or deviations in large-scale datasets has significant implications for decision-making and operational efficiency. This thesis focuses on the detection of outliers in hierarchical, high-dimensional time series data. The dataset presents a unique challenge due to its hierarchical structure of multiple stores and departments with interdependent sales patterns over time, making it a complex time series problem. In the first part of this thesis, a comprehensive review of theory and anomaly detection models is conducted, assessing their suitability for large scale hierarchical time series data. Based on the findings from the research phase, anomaly detection methods are selected and implemented on the dataset. The second part of the thesis applies these approaches to the data, comparing their effectiveness in identifying significant outliers. The results are then analyzed to assess their practical implications for real-world applications. This study contributes to the field by identifying robust methodologies for outlier detection in hierarchical time series data and providing actionable insights for businesses dealing with large-scale sales forecasting. The findings can support more informed decision-making, reducing financial losses and enhancing operational strategies. Point-wise anomaly detection, large-scale hierarchical multivariate time series, decomposition, unsupervised models, interpretability, scalability, evaluation, precision, hierarchical reconciliation. Způsob přístupu: Internet data a analytika pro business [obor dipl. práce] diplomové práce fd132022 czenas master's theses eczenas Precision Point-wise anomaly detection Scalability Evaluation Interpretability Hierarchical reconciliation Large-scale hierarchical multivariate time series Decomposition Unsupervised models Zimmermann, Pavel, 1980- jo20251268570 ths Roček, Vojtěch opn Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky kn20010709399 dgg https://insis.vse.cz/zp/92246/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/92246 Hlavní práce https://insis.vse.cz/zp/92246/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/92246/posudek/oponent/86781 Oponentura https://insis.vse.cz/zp/92246/priloha/31064 Přiloha k práci https://insis.vse.cz/zp/92246/priloha/31065 Přiloha k práci https://insis.vse.cz/zp/92246/priloha/31066 Přiloha k práci https://insis.vse.cz/zp/92246/priloha/31067 Přiloha k práci https://insis.vse.cz/zp/92246/priloha/31068 Přiloha k práci https://insis.vse.cz/zp/92246/podrobnosti dc:identifier NEPOSILAT VSKP vse92246 250605 92246