Essays about: "syntetiska anomalier"
Found 5 essays containing the words syntetiska anomalier.
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1. Unsupervised Machine Learning Based Anomaly Detection in Stockholm Road Traffic
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : This thesis is a study of anomaly detection in vehicle traffic data in central Stockholm. Anomaly detection is an important tool in the analysis of traffic data for improved urban planing. Two unsupervised machine learning models are used, the DBSCAN clustering model and the LSTM deep learning neural network. READ MORE
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2. A Review of Anomaly Detection Techniques forHeterogeneous Datasets
University essay from KTH/Optimeringslära och systemteoriAbstract : Anomaly detection is a field of study that is closely associated with machine learning and it is the process of finding irregularities in datasets. Developing and maintaining multiple machine learning models for anomaly detection takes time and can be an expensive task. One proposed solution is to combine all datasets and create a single model. READ MORE
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3. Unsupervised Anomaly Detection on Time Series Data: An Implementation on Electricity Consumption Series
University essay from KTH/Matematisk statistikAbstract : Digitization of the energy industry, introduction of smart grids and increasing regulation of electricity consumption metering have resulted in vast amounts of electricity data. This data presents a unique opportunity to understand the electricity usage and to make it more efficient, reducing electricity consumption and carbon emissions. READ MORE
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4. Outlier detection for overnight index swaps
University essay from Umeå universitet/Institutionen för matematik och matematisk statistikAbstract : I examensarbetet undersöks metoder för anomalidetektion i tidsserie data. Givet data för overnight index swaps (SEK), så har syntetiskt data skapats med olika ty-per av anomalier. READ MORE
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5. Unsupervised real-time anomaly detection on streaming data for large-scale application deployments
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Anomaly detection is the classification of data points that do not adhere to the familiar pattern; in previous studies there existed a need for extensive human interactions with either labelling or sorting normal and abnormal data from one another. In this thesis, we want to go one step further and apply machine learning techniques on time-series data in order to have a deeper understanding of the properties of a given data point without any sorting and labelling. READ MORE
