This thesis explores anomaly detection in time series data, a crucial aspect in various fields such as finance, healthcare, and environmental monitoring. Modern technology allows for the collection...Show moreThis thesis explores anomaly detection in time series data, a crucial aspect in various fields such as finance, healthcare, and environmental monitoring. Modern technology allows for the collection of vast amounts of time-dependent data, which captures the evolution of variables over time. Our primary focus is on detecting anomalies, or abnormal observations, in time series, where statistical properties like mean and variance remain constant over time. We propose a robust exponential smoothing algorithm to detect anomalies, improving upon the traditional non-robust methods by incorporating robust statistics to handle outliers effectively. The algorithm is evaluated using two metrics: true positive rate and false positive rate, presented through Receiver Operating Characteristic (ROC) curves. Our results demonstrate that the robust exponential smoothing algorithm outperforms the non-robust version, particularly in contexts involving procurement data. We also discuss the impact of tuning parameters on the algorithm's performance. Simulation of dummy data, mimicking the structure and behavior of real procurement datasets, allows us to test the algorithm's effectiveness in identifying various types of anomalies. This thesis contributes to the field of anomaly detection by offering a robust method adaptable to different datasets and contexts, ensuring reliable and accurate results even when data contains irregularities. Future work could involve enhancing the algorithm's runtime and exploring automated report generation using large language models.Show less
Understanding the functioning of the brain and how it relates to behavior is one of theprimary objectives of neuroscience. The focus of neuroscience has evolved from a singlebrain to studying...Show moreUnderstanding the functioning of the brain and how it relates to behavior is one of theprimary objectives of neuroscience. The focus of neuroscience has evolved from a singlebrain to studying interactions between multiple brains. In several fields, synchrony inbrain responses between individuals has been proven to positively influence psychologicalprocesses and lead to better outcomes.Time-series data for each subject’s behavior or modality are obtained by measuringsynchrony. Comparative studies for synchrony methods have been carried out in orderto gain some insight into the similarities and differences between many measures forevaluating the synchrony between subjects using such time-series data. The research onlyprovides a partial picture of the performance of the synchrony methods in terms of captur-ing synchrony and the conditions in which these methods are optimal. It is still unknownhow well the synchrony methods perform when changing other data characteristics.The goal of this study is to evaluate the performance of several methods for capturingdifferent types of synchrony between a pair of time series. Two mechanisms are usedto generate a pair of time-series data with a known amount of synchrony between thetime series (1) two unidirectionally connected Hénon maps, and (2) bivariate von Misesdistribution. Correlations between the two time series are computed as another definitionof true synchrony to provide a different perspective on true synchrony. In addition, asystematical evaluation of the performance of the synchrony methods on simulated datawith various data characteristics is carried out.For the generated data coherence and phase synchrony are the two best performingmethods. Regarding the varied data characteristics, especially the amount of true syn-chrony has a large effect on recovery performance. These main effects between the datacharacteristics are qualified by several two-way and three-way interactions that almostalways include the synchrony methods and the amount of true synchrony. Under all ofthe different data characteristics, no synchrony method is perfect, and all of the synchronymethods in this study are not always stable. As a result, using a combination of differentsynchrony methods to detect synchrony is recommended.Show less
This project addresses two methods of bloodstain dating for forensic investigations. Firstly and briefly, AFM-based nanoindentation and probe-related uncertainties will be discussed. A self-written...Show moreThis project addresses two methods of bloodstain dating for forensic investigations. Firstly and briefly, AFM-based nanoindentation and probe-related uncertainties will be discussed. A self-written Python program to assist in the determination of the AFM probe's tip radius will be presented. The emphasis of this project lies on the second method: digital image analysis. Bloodstains in controlled environments (temperature and humidity) are imaged in time series. From the obtained images, the mean color values of the bloodstain in function of time are extracted. Two optical imaging time series are performed that provide a proof of concept: the color of bloodstains changes in function of time and this change is observable using a microscope camera and image processing with Python. The behavior of the color changes will be characterized by considering the changes in different color models (RGB, HSV, CMYK, CIELAB).Show less
