Sökning: "Multivariata tidsserier"

Visar resultat 1 - 5 av 11 uppsatser innehållade orden Multivariata tidsserier.

  1. 1. Anomaly detection for prediction of failures in manufacturing environments : Machine learning based semi-supervised anomaly detection for multivariate time series to predict failures in a CNC-machine

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Felix Boltshauser; [2023]
    Nyckelord :Machine learning; Anomaly Detection; DeepAnT; ROCKET; OCSVM; manufacturing; predictive maintenance; Maskin inlärning; Anomali Detektion; DeepAnT; ROCKET; OCSVM; tillverkning; prediktivt underhåll;

    Sammanfattning : For manufacturing enterprises, the potential of collecting large amounts of data from production processes has enabled the usage of machine learning for prediction-based monitoring and maintenance of machines. Yet common maintenance strategies still include reactive handling of machine failures or schedule-based maintenance conducted by experienced personnel. LÄS MER

  3. 2. Sign of the Times : Unmasking Deep Learning for Time Series Anomaly Detection

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Daniel Richards Ravi Arputharaj; [2023]
    Nyckelord :Anomaly detection; multivariate time series data; deep learning models; model complexity; resource-constrained systems; Variational Autoencoders VAEs ; Convolutional Variational Autoencoders; evaluation metrics in time series; Anomalidetektering; Multivariata tidsseriedata; Djupinlärningsmodeller; Modellkomplexitet; Resursbegränsade system; Variational Autoencoders VAEs ; Konvolutionella Variational Autoencoders; Utvärderingsmått inom tidsserier;

    Sammanfattning : Time series anomaly detection has been a longstanding area of research with applications across various domains. In recent years, there has been a surge of interest in applying deep learning models to this problem domain. LÄS MER

  4. 3. Banger for the Buck : Predicting Growth of Music Tracks using Machine Learning

    Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Elliot Nilsson; Liza Wensink; [2022]
    Nyckelord :Time series classification; Multivariate time series; Music industry; Record label business model.;

    Sammanfattning : The advent of music streaming has made it increasingly important for actors in the music industry to understand if tracks are going to succeed or not. This study investigates if it is possible to accurately classify the growth of the listener base of a music track based on multivariate time series with listener behavior data. LÄS MER

  5. 4. VePMAD: A Vehicular Platoon Management Anomaly Detection System : A Case Study of Car-following Mode, Middle Join and Exit Maneuvers

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Weaam Bayaa; [2021]
    Nyckelord :MDS; GMMHMM; PMP; Machine Learning; Bayesian information Criterion BIC ; Platoon Behavior Recognition; MDS; GMMHMM; PMP; Maskininlärnings; BIC; Plutonbeteendeigenkänning;

    Sammanfattning : Vehicle communication using sensors and wireless channels plays an important role to allow exchanging information. Adding more components to allow exchanging more information with infrastructure enhanced the capabilities of vehicles and enabled the rise of Cooperative Intelligent Transport Systems (C-ITS). LÄS MER

  7. 5. A Deep Learning Approach to Predicting the Length of Stay of Newborns in the Neonatal Intensive Care Unit

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Bas Theodoor Straathof; [2020]
    Nyckelord :Deep Neural Networks; Electronic Health Records; Length-of-Stay Prediction; Multivariate Time Series Classification; Djupa Neurala Nätverk; Elektroniska Hälsoregister; Klassificering av Multivariat Tidsserie; Förutsägelse av Vistelsetid;

    Sammanfattning : Recent advancements in machine learning and the widespread adoption of electronic healthrecords have enabled breakthroughs for several predictive modelling tasks in health care. One such task that has seen considerable improvements brought by deep neural networks is length of stay (LOS) prediction, in which research has mainly focused on adult patients in the intensive care unit. LÄS MER