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Showing result 1 - 5 of 87 essays matching the above criteria.
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1. Spatio-temporal analysis of COVID-19 in Västra Götaland, Sweden
University essay from Göteborgs universitet/Institutionen för matematiska vetenskaperAbstract : Spatio-temporal analysis of COVID-19 data with the two different statistical approaches is the main objective of this thesis. The first classical approach, the Endemic-Epidemic framework (Held et al., 2005) is a class of multivariate time-series models for the incidence counts, obtained from the surveillance systems. READ MORE
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2. Neural Network-based Anomaly Detection Models and Interpretability Methods for Multivariate Time Series Data
University essay from Stockholms universitet/Institutionen för data- och systemvetenskapAbstract : 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. READ MORE
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3. Restaurant Daily Revenue Prediction : Utilizing Synthetic Time Series Data for Improved Model Performance
University essay from Uppsala universitet/Avdelningen för beräkningsvetenskapAbstract : This study aims to enhance the accuracy of a demand forecasting model, XGBoost, by incorporating synthetic multivariate restaurant time series data during the training process. The research addresses the limited availability of training data by generating synthetic data using TimeGAN, a generative adversarial deep neural network tailored for time series data. READ MORE
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4. Preserving Privacy in Cloud Services by Using an Explainable Deep-Learning Model for Anomaly Detection
University essay from Linköpings universitet/Institutionen för datavetenskapAbstract : As cloud services become increasingly popular, ensuring their privacy and security has become a significant concern for users. Cloud computing involves Data Service Outsourcing and Computation Outsourcing, which require additional security considerations compared to traditional computing. READ MORE
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5. Evaluating Brain-Inspired Machine Learning Models for Time Series Forecasting: A Comparative Study on Dynamical Memory in Reservoir Computing and Neural Networks
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Brain-inspired computing is a promising research field, with potential to encouragebreakthroughs within machine learning and enable us to solve complex problems in a moreefficient way. This study aims to compare the performance of brain-like machine learningalgorithms for time series forecasting. READ MORE