The acquisition of remote sensing data having an investigated quality level constitutes an important step to advance our understanding of the vegetation response to environmental factors. Spaceborne sensors introduce additional challenges that should be addressed to assure that derived findings are based on real phenomena, and not biased or misguided by instrument features or processing artifacts. As a consequence, updates to incorporate new advances and user requirements are regularly found on most cutting edge systems such as the MODIS system. In this dissertation, the objective was to design, characterize and assess any possible departure from current values, a MODIS VI algorithm for restoring the continuity 16-day 1-km product, based on the new 8-day 500-m MODIS SR product scheduled for MODIS C6. Additionally, the impact of increasing the time resolution from 16 to 8 days for the future basic MODIS C6 VI product was also assessed. The performance of the proposed algorithm was evaluated using high quality reference data and known biophysical relationships at several spatial and temporal scales. Firstly, it was evaluated using data from the ASRVN, FLUXNET-derived ecosystem GPP and an analysis of the seasonality parameters derived from current C5 and proxy C6 VI collections. The performance of the 8-day VI version was evaluated and contrasted with current 16-day using the reported correlation of the EVI with the GPP derived from CO2 flux measurements. Secondly, we performed an analysis at spatial level using entire images (or "tiles") to assess the BRDF effects on the VI product, as these can cause biases on the SR and VIs from scanning radiometers. Lastly, we evaluated the performance of the proposed algorithm for detecting inter-annual VI anomalies from long-term time series, as compared with current MODIS VI C5. For this, we analyzed the EVI anomalies from a densely vegetated evergreen region, for the period July-September (2000-2010). Results showed a high general similarity between results from both algorithms, but also systematic differences, suggesting that proposed algorithm towards C6 may represent an advance in the reduction of uncertainties for the MODIS VI product.

In this study, it is determined that excess returns on the OMX 30 are confirmed to rise in anticipation of monetary policy decisions made by the central banks of Sweden and The United States of America. Those findings were manifested at a greater magnitude on the first day prior to the announcements and on a statistically significant level one day prior to monetary policy decisions from the Federal Open Market Committee. Moreover, excess returns beyond the average rate were found to be substantially higher on the first and third day prior monetary policy decisions from the Swedish Central bank (Riksbanken) albeit not on a statistically significant level. The results drawn from the data in the study were reinforced by findings in similar tests conducted during times of global recession.

In the automotive industry test drives are conducted during the development of new vehicle models or as a part of quality assurance for series vehicles. During the test drives, data is recorded for the use of fault analysis resulting in millions of data points. Since multiple vehicles are tested in parallel, the amount of data that is to be analysed is tremendous. Hence, manually analysing each recording is not feasible. Furthermore the complexity of vehicles is ever-increasing leading to an increase of the data volume and complexity of the recordings. Only by effective means of analysing the recordings, one can make sure that the effort put in the conducting of test drives pays off. Consequently, effective means of test drive analysis can become a competitive advantage. This Thesis researches ways to detect unknown or unmodelled faults in recordings from test drives with the following two aims: (1) in a data base of recordings, the expert shall be pointed to potential errors by reporting anomalies, and (2) the time required for the manual analysis of one recording shall be shortened. The idea to achieve the first aim is to learn the normal behaviour from a training set of recordings and then to autonomously detect anomalies. The one-class classifier “support vector data description” (SVDD) is identified to be most suitable, though it suffers from the need to specify parameters beforehand. One main contribution of this Thesis is a new autonomous parameter tuning approach, making SVDD applicable to the problem at hand. Another vital contribution is a novel approach enhancing SVDD to work with multivariate time series. The outcome is the classifier “SVDDsubseq” that is directly applicable to test drive data, without the need for expert knowledge to configure or tune the classifier. The second aim is achieved by adapting visual data mining techniques to make the manual analysis of test drives more efficient. The methods of “parallel coordinates” and “scatter plot matrices” are enhanced by sophisticated filter and query operations, combined with a query tool that allows to graphically formulate search patterns. As a combination of the autonomous classifier “SVDDsubseq” and user-driven visual data mining techniques, a novel, data-driven, semi-autonomous approach to detect unmodelled faults in recordings from test drives is proposed and successfully validated on recordings from test drives. The methodologies in this Thesis can be used as a guideline when setting up an anomaly detection system for own vehicle data.

Les oxydes d’azote atmosphériques (NOx=NO+NO2) sont des composés clefs en chimie de l’environnement, jouant un rôle central pour la capacité oxydante de l’atmosphère et le cycle de l’azote. La composition isotopique du nitrate atmosphérique (NO?3 particulaire et HNO3 gazeux), constituant leur puits ultime, renseigne sur leur bilan chimique. Le rapport 15N/14N donne une indication de leurs sources, alors que l’anomalie isotopique en oxygène (?17O=d17O-0.52×d18O) révèle la nature de leurs mécanismes d’oxydation. Des études couplées de d15N et ?17O d’échantillons de nitrate atmosphérique collectés dans l’Arctique, en Antarctique et dans l’atmosphère marine au dessus de l’Océan Atlantique, où le bilan des NOx est souvent mal connu ont été effectuées. À ces fins, le défi que constitue la mesure simultanée des trois rapports isotopiques du nitrate (17O/16O, 18O/16O et 15N/14N) dans le même échantillon représentant moins d’une micromole a été relevé. La solution adoptée tire avantage des propriétés d’une bactérie dénitrifiante, utilisée pour convertir le nitrate en N2O, dont la composition isotopique totale a été mesurée en utilisant un système automatisé de chromatographie en phase gazeuse et spectrométrie de masse de rapport isotopique. Les principaux résultats obtenus via les isotopes de l’oxygène permettent l’identification claire de transitions saisonnières entre voies d’oxydation des NOx, y compris le rôle majeur des composés halogénés réactifs au printemps polaire en régions côtières. Les isotopes de l’azote ont quant à eux permis d’apporter de nouvelles contraintes sur le cycle de l’azote dans les régions polaires, grâce au fractionnement significatif induit par les phénomènes de remobilisation post-dépôt affectant le nitrate dans le manteau neigeux, et l’émission de NOx qui en découle / Atmospheric nitrogen oxides (NOx=NO+NO2) are central to the chemistry of the environment, as they play a pivotal role in the cycling of reactive nitrogen and the oxidative capacity of the atmosphere. The stable isotopes of atmospheric nitrate (in the form of particulate NO?3 or gaseous HNO3), their main ultimate sinks, provide insights in chemical budget of NOx : its nitrogen isotopes are almost conservative tracers of their sources, whereas NOx sinks are revealed by its triple oxygen isotopic composition. The long-awaited challenge of measuring all three stable isotope ratios of nitrate (17O/16O, 18O/16O and 15N/14N) in a single sample at sub-micromolar levels has been resolved. The newly developed method makes use of denitrifying bacteria to quantitatively convert nitrate to a stable species (N2O), whose isotope ratios are measured using an automated gas chromatography/isotope ratio mass spectrometry analytical system. Dual measurements of d15N and the isotope anomaly (?17O=d17O-0.52×d18O) of atmospheric nitrate samples collected in the Arctic, the Antarctic and in the marine boundary layer of the Atlantic Ocean, have been used to derive the chemical budget of NOx and atmospheric nitrate in these remote regions. Main results from oxygen isotope measurements pertain to the identification of seasonal and latitudinal shifts in NOx oxidative pathways in these environments (including the role of halogen oxides chemistry in polar regions during springtime), as a function of particle sizes. Nitrogen isotopes are found to provide strong constraints on the budget of reactive nitrogen in polar regions, due to the strong fractionation associated with snowpack photochemical loss of nitrate and its conversion to NOx

Cette thèse de doctorat consiste en trois chapitres étudiant les liens transnationaux dans différents groupes d’économies industrialisées. Le premier chapitre montre que depuis le milieu des années 1980 et 1990, la part de la variance du cycle de l’investissement des entreprises due aux facteurs communs internationaux a augmenté aux États-Unis ainsi que dans les principaux pays Européens. Le second chapitre estime l’impact de la libéralisation et de l’internationalisation des secteurs bancaires et financiers sur les variations communes de la croissance du PIB réel. Depuis la fin des années 1970, un facteur commun international a contribué à la majorité de la croissance économique dans les pays de l’UE, les États-Unis, le Canada et le Japon. Parmi plusieurs indicateurs financiers, bancaires et monétaires, les prix des actions suivi des investissements de portefeuille ont été de loin les principaux déterminants de ce facteur. La suppression des contrôles sur le crédit domestique apparaît comme la seule mesure de libéralisation financière ayant eu un effet substantiel et négatif sur la croissance commune avant 1995. Le troisième chapitre étudie les sources de covariations du PIB réel entre les pays fondateurs de la zone euro. Tout au long de l’UEM, la synchronisation des cycles réels a été robustement reliée aux disparités en matière de politique budgétaire et de gains de productivité totale des facteurs. La synchronisation des cycles était étroitement associée à la similarité de la croissance des coûts salariaux unitaires avant 2007, mais non après 2007 lorsque les différentiels entre les taux d’intérêt à long terme sont devenus une cause majeure de divergence cyclique. / This doctoral thesis consists in three chapters investigating cross-country linkages in different samples of industrialized economies. The first chapter shows that the share of the investment cycle's variance due to common international factors has increased in the United States as well in large European countries. The second chapter estimates the impact of the liberalization and internationalization of the financial and banking sectors on real GDP growth comovements. Since the late 1970s, a common international factor has contribued to most economic growth in th EU countries, the United States, Canada and Japan. Among several financial, bank and monetary indicators, equity prices, followed by portofolio investment have been by far the main drivers of this factor. The removal of controls on domestic credit emerges as the only financial liberalization policy measure with a large and negative effect on common growth before 1995. The third chapter investigates the sources of real GDP's comovements between the founding member states of the euro area. Throughout EMU, real cyclical synchronization was robustly linked to disparities in term of fiscal policy and of total factor productivity gains. Cyclical synchronization was closely related to similarities in unit labour cost growth before 2007, but not after 2007 when long-term interest rate differentials became a major cause of cyclical divergence.

Background: Detecting anomalies in time-series data is a task that can be done with the help of data driven machine learning models. This thesis will investigate if, and how well, different machine learning models, with an unsupervised approach,can detect anomalies in the e-Transaction system Ericsson Wallet Platform. The anomalies in our domain context is delays on the system. Objectives: The objectives of this thesis work is to compare four different machine learning models ,in order to find the most relevant model. The best performing models are decided by the evaluation metric F1-score. An intersection of the best models are also being evaluated in order to decrease the number of False positives in order to make the model more precise. Methods: Investigating a relevant time-series data sample with 10-minutes interval data points from the Ericsson Wallet Platform was used. A number of steps were taken such as, handling data, pre-processing, normalization, training and evaluation.Two relevant features was trained separately as one-dimensional data sets. The two features that are relevant when finding delays in the system which was used in this thesis is the Mean wait (ms) and the feature Mean * N were the N is equal to the Number of calls to the system. The evaluation metrics that was used are True positives, True Negatives, False positives, False Negatives, Accuracy, Precision, Recall, F1-score and Jaccard index. The Jaccard index is a metric which will reveal how similar each algorithm are at their detection. Since the detection are binary, it’s classifying the each data point in the time-series data. Results: The results reveals the two best performing models regards to the F1-score.The intersection evaluation reveals if and how well a combination of the two best performing models can reduce the number of False positives. Conclusions: The conclusion to this work is that some algorithms perform better than others. It is a proof of concept that such classification algorithms can separate normal from non-normal behavior in the domain of the Ericsson Wallet Platform.

Machine diagnostics is usually done via conditioned monitoring (CM). This approach analyses certain thresholds or patterns for diagnostic purposes. This approach can be costly and time consuming for industries. A larger downside is the difficulty in generalizing CM to a wider set of machines.There is a new trend of using a Machine learning (ML) approach to diagnose machines states. An ML approach would implement an autonomous system for diagnosing machines. It is highly desirable within industry to replace the manual labor performed when setting up CBM systems. Often the ML algorithms chosen are novelty/anomaly based. It is a popular hypothesis that detecting anomalous measurements from a system is a natural byproduct of a machine in a faulty state.The purpose of this thesis is to help CombiQ with an implementation strategy for a fault detection system. The idea of the fault detection system is to make prediction outcomes for machines within the system. More specifically, the prediction will inform whether a machine is in a faulty state or a normal state. An ML approach will be implemented to predict anomalous measurements that corresponds to a faulty state. The system will have no previous data on the machines. However, data for a machine will be acquired once sensors (designed by CombiQ) have been set up for the machine.The results of the thesis proposes an unsupervised and semi-supervised approach for creating the ML models used for the fault detection system. The unsupervised approach will rely on assumptions when selecting the hyperparameters for the ML. The semi-supervised approach will try to learn the hyperparameters through cross validation and grid search. An experiment was set up check whether three ML algorithms can learn optimal hyperparameter values for predicting rotational unbalance. The algorithm known as OneClassVM showed the best precision results and hence proved more useful for CombiQ’s criterium.

The financial markets are no longer what they used to be and one reason for this is the breakthrough of algorithmic trading. Although this has had several positive effects, there have been recorded incidents where algorithms have been involved. It is therefore of interest to find effective methods to monitor algorithmic trading. The purpose of this thesis was therefore to contribute to this research area by investigating if machine learning can be used for detecting deviating behavior. Since the real world data set used in this study lacked labels, an unsupervised anomaly detection approach was chosen. Two models, isolation forest and deep denoising autoencoder, were selected and evaluated. Because the data set lacked labels, artificial anomalies were injected into the data set to make evaluation of the models possible. These synthetic anomalies were generated by two different approaches, one based on a downsampling strategy and one based on manual construction and modification of real data. The evaluation of the anomaly detection models shows that both isolation forest and deep denoising autoencoder outperform a trivial baseline model, and have the ability to detect deviating behavior. Furthermore, it is shown that a deep denoising autoencoder outperforms isolation forest, with respect to both area under the receiver operating characteristics curve and area under the precision-recall curve. A deep denoising autoencoder is therefore recommended for the purpose of algorithmic trading surveillance.

<div> <p>Sound and vibration analysis is a prominent tool used for scientific investigations in various fields such as structural model identification or dynamic behavior studies. In manufacturing fields, the vibration signals collected through commercial sensors are utilized to monitor machine health, for sustainable and cost-effective manufacturing.</p> <p> Recently, the development of commercial sensors and computing environments have encouraged researchers to combine gathered data and Machine Learning (ML) techniques, which have been proven to be efficient for categorical classification problems. These discriminative algorithms have been successfully implemented in monitoring problems in factories, by simulating faulty situations. However, it is difficult to identify all the sources of anomalies in a real environment. </p> <p>In this paper, a Neural Network (NN) application on a KUKA KR6 robot arm is introduced, as a solution for the limitations described above. Specifically, the autoencoder architecture was implemented for anomaly detection, which does not require the predefinition of faulty signals in the training process. In addition, stethoscopes were utilized as alternative sensing tools as they are easy to handle, and they provide a cost-effective monitoring solution. To simulate the normal and abnormal conditions, different load levels were assigned at the end of the robot arm according to the load capacity. Sound signals were recorded from joints of the robot arm, then meaningful features were extracted from spectrograms of the sound signals. The features were utilized to train and test autoencoders. During the autoencoder process, reconstruction errors (REs) between the autoencoder’s input and output were computed. Since autoencoders were trained only with features corresponding to normal conditions, RE values corresponding to abnormal features tend to be higher than those of normal features. In each autoencoder, distributions of the RE values were compared to set a threshold, which distinguishes abnormal states from the normal states. As a result, it is suggested that the threshold of RE values can be utilized to determine the condition of the robot arm.</p> </div> <br>

La détection d'anomalies est une tâche cruciale qui a suscité l'intérêt de plusieurs travaux de recherche dans les communautés d'apprentissage automatique et fouille de données. La complexité de cette tâche dépend de la nature des données, de la disponibilité de leur étiquetage et du cadre applicatif dont elles s'inscrivent. Dans le cadre de cette thèse, nous nous intéressons à cette problématique pour les données complexes et particulièrement pour les séries temporelles uni et multi-variées. Le terme "anomalie" peut désigner une observation qui s'écarte des autres observations au point d'éveiller des soupçons. De façon plus générale, la problématique sous-jacente (aussi appelée détection de nouveautés ou détection des valeurs aberrantes) vise à identifier, dans un ensemble de données, celles qui différent significativement des autres, qui ne se conforment pas à un "comportement attendu" (à définir ou à apprendre automatiquement), et qui indiquent un processus de génération différent. Les motifs "anormaux" ainsi détectés se traduisent souvent par de l'information critique. Nous nous focalisons plus précisément sur deux aspects particuliers de la détection d'anomalies à partir de séries temporelles dans un mode non-supervisé. Le premier est global et consiste à ressortir des séries relativement anormales par rapport une base entière. Le second est dit contextuel et vise à détecter localement, les points anormaux par rapport à la structure de la série étudiée. Pour ce faire, nous proposons des approches d'optimisation à base de clustering pondéré et de déformation temporelle pour la détection globale ; et des mécanismes à base de modélisation matricielle pour la détection contextuelle. Enfin, nous présentons une série d'études empiriques sur des données publiques pour valider les approches proposées et les comparer avec d'autres approches connues dans la littérature. De plus, une validation expérimentale est fournie sur un problème réel, concernant la détection de séries de prix aberrants sur les pneumatiques, pour répondre aux besoins exprimés par le partenaire industriel de cette thèse / Anomaly detection is a crucial task that has attracted the interest of several research studies in machine learning and data mining communities. The complexity of this task depends on the nature of the data, the availability of their labeling and the application framework on which they depend. As part of this thesis, we address this problem for complex data and particularly for uni and multivariate time series. The term "anomaly" can refer to an observation that deviates from other observations so as to arouse suspicion that it was generated by a different generation process. More generally, the underlying problem (also called novelty detection or outlier detection) aims to identify, in a set of data, those which differ significantly from others, which do not conform to an "expected behavior" (which could be defined or learned), and which indicate a different mechanism. The "abnormal" patterns thus detected often result in critical information. We focus specifically on two particular aspects of anomaly detection from time series in an unsupervised fashion. The first is global and consists in detecting abnormal time series compared to an entire database, whereas the second one is called contextual and aims to detect locally, the abnormal points with respect to the global structure of the relevant time series. To this end, we propose an optimization approaches based on weighted clustering and the warping time for global detection ; and matrix-based modeling for the contextual detection. Finally, we present several empirical studies on public data to validate the proposed approaches and compare them with other known approaches in the literature. In addition, an experimental validation is provided on a real problem, concerning the detection of outlier price time series on the tyre data, to meet the needs expressed by, LIZEO, the industrial partner of this thesis