Avancerad sökning

Visar resultat 1 - 5 av 26 uppsatser som matchar ovanstående sökkriterier.

  1. 1. Neural Network-based Anomaly Detection Models and Interpretability Methods for Multivariate Time Series Data

    Master-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskap

    Författare :Deepthy Prasad; Swathi Hampapura Sripada; [2023]
    Nyckelord :multivariate - time series; anomaly detection; neural networks; autoencoders; interpretability; counterfactuals;

    Sammanfattning : Anomaly detection plays a crucial role in various domains, such as transportation, cybersecurity, and industrial monitoring, where the timely identification of unusual patterns or outliers is of utmost importance. Traditional statistical techniques have limitations in handling complex and highdimensional data, which motivates the use of deep learning approaches. LÄS MER

  3. 2. Robust Statistical Jump Models with Feature Selection

    Master-uppsats, Lunds universitet/Matematisk statistik

    Författare :Jonatan Persson; [2023]
    Nyckelord :Clustering; Jump; Feature selection; Robust; Mathematics and Statistics;

    Sammanfattning : A large area in statistics and machine learning is cluster analysis. This field of research concerns the design of algorithms that allow computers to automatically categorize a set of observations into different groups in a reasonable way, without any prior information about which observations belongs to which group. LÄS MER

  4. 3. Dataset characteristics effect on time series forecasting : comparison of statistical and deep learning models

    Kandidat-uppsats, Högskolan i Halmstad/Akademin för informationsteknologi

    Författare :Adam Ahlman; Adam Taylor; [2023]
    Nyckelord :Time Series; Forecasting;

    Sammanfattning : Time series are points of data measured throughout time in equally spaced periods. They present characteristics such as level, noise, trend, seasonality, and outliers. Time series forecasting is the attempt to predict single or multiple future values. LÄS MER

  5. 4. Evaluating clustering techniques in financial time series

    Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Avdelningen för systemteknik

    Författare :Johan Millberg; [2023]
    Nyckelord :clustering; machine learning; financial time series; time series; unsupervised learning; cluster validation; cluster evaluation; klustring; klusteranalys; finansiella tidsserier; maskininlärning; klustervalidering; evalueringsteknik;

    Sammanfattning : This degree project aims to investigate different evaluation strategies for clustering methodsused to cluster multivariate financial time series. Clustering is a type of data mining techniquewith the purpose of partitioning a data set based on similarity to data points in the same cluster,and dissimilarity to data points in other clusters. LÄS MER

  7. 5. Evaluating the use of Brush and Tooltip for Time Series visualizations: A comparative study

    Kandidat-uppsats, Malmö universitet/Fakulteten för teknik och samhälle (TS)

    Författare :Sebastian Helin; André Eklund; [2023]
    Nyckelord :Graphical Perception Tooltip Brush Accuracy Estimation Time Univariate Multivariate Time Series Visualization;

    Sammanfattning : This study uses a combination of user testing and analysis to evaluate the impact of brush and tooltip on the comprehension of time series visualizations. Employing a sequential mixed-methods approach, with qualitative data from semi-structured interviews used to inform the design of a visualization tool, followed by a quantitative user study to validate it. LÄS MER