Essays about: "numeriska och kategoriska tidsserier"

Found 3 essays containing the words numeriska och kategoriska tidsserier.

  1. 1. Detecting Faults in Telecom Software Using Diffusion Models : A proof of concept study for the application of diffusion models on Telecom data

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Mohamad Nabeel; [2023]
    Keywords : Diffusion models; Anomaly Detection; Telecommunication; Time Series; Diffusionsmodeller; Anomalitetsdetektering; Telekommunikation; Tidsserier;

    Abstract : This thesis focuses on software fault detection in the telecom industry, which is crucial for companies like Ericsson to ensure stable and reliable software. Given the importance of software performance to companies that rely on it, automatically detecting faulty behavior in test or operational environments is challenging. READ MORE

  3. 2. Clustering of Unevenly Spaced Mixed Data Time Series

    University essay from KTH/Matematisk statistik

    Author : Pierre Sinander; Asik Ahmed; [2023]
    Keywords : mixed data time series; unevenly spaced time series; clustering; dynamic time warping; Gower dissimilarity; time warping regularisation; numeriska och kategoriska tidsserier; ojämnt fördelade tidsserier; kluster analys; dynamic time warping; Gower dissimilaritet; regularisering av tidsförvränging;

    Abstract : This thesis explores the feasibility of clustering mixed data and unevenly spaced time series for customer segmentation. The proposed method implements the Gower dissimilarity as the local distance function in dynamic time warping to calculate dissimilarities between mixed data time series. READ MORE

  4. 3. Detecting Performance Anomalies in a Mobile Application with Unsupervised Machine Learning

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Lukas Saari; [2019]
    Keywords : ;

    Abstract : Unsupervised anomaly detection algorithms are applied with the purpose of identifying performance regressions in a mobile application. To evaluate the performance, a labeled artificial data set is generated that is based on a real data set and that aims to reflect its properties. READ MORE