Financial time series analysis with competitive neural networks

L’objectif principal de mémoire est la modélisation des données temporelles non stationnaires. Bien que les modèles statistiques classiques tentent de corriger les données non stationnaires en différenciant et en ajustant pour la tendance, je tente de créer des grappes localisées de données de séries temporelles stationnaires grâce à l’algorithme du « self-organizing map ». Bien que de nombreuses techniques aient été développées pour les séries chronologiques à l’aide du « self- organizing map », je tente de construire un cadre mathématique qui justifie son utilisation dans la prévision des séries chronologiques financières. De plus, je compare les méthodes de prévision existantes à l’aide du SOM avec celles pour lesquelles un cadre mathématique a été développé et qui n’ont pas été appliquées dans un contexte de prévision. Je compare ces méthodes avec la méthode ARIMA bien connue pour la prévision des séries chronologiques. Le deuxième objectif de mémoire est de démontrer la capacité du « self-organizing map » à regrouper des données vectorielles, puisqu’elle a été développée à l’origine comme un réseau neuronal avec l’objectif de regroupement. Plus précisément, je démontrerai ses capacités de regroupement sur les données du « limit order book » et présenterai diverses méthodes de visualisation de ses sorties.The main objective of this Master’s thesis is in the modelling of non-stationary time series data. While classical statistical models attempt to correct non- stationary data through differencing and de-trending, I attempt to create localized clusters of stationary time series data through the use of the self-organizing map algorithm. While numerous techniques have been developed that model time series using the self-organizing map, I attempt to build a mathematical framework that justifies its use in the forecasting of financial times series. Additionally, I compare existing forecasting methods using the SOM with those for which a framework has been developed and which have not been applied in a forecasting context. I then compare these methods with the well known ARIMA method of time series forecasting. The second objective of this thesis is to demonstrate the self-organizing map’s ability to cluster data vectors as it was originally developed as a neural network approach to clustering. Specifically I will demonstrate its clustering abilities on limit order book data and present various visualization methods of its output

A Framework and Classification for Fault Detection Approaches in Wireless Sensor Networks with an Energy Efficiency Perspective

Wireless Sensor Networks (WSNs) are more and more considered a key enabling technology for the realisation of the Internet of Things (IoT) vision. With the long term goal of designing fault-tolerant IoT systems, this paper proposes a fault detection framework for WSNs with the perspective of energy efficiency to facilitate the design of fault detection methods and the evaluation of their energy efficiency. Following the same design principle of the fault detection framework, the paper proposes a classification for fault detection approaches. The classification is applied to a number of fault detection approaches for the comparison of several characteristics, namely, energy efficiency, correlation model, evaluation method, and detection accuracy. The design guidelines given in this paper aim at providing an insight into better design of energy-efficient detection approaches in resource-constraint WSNs

Valores aberrantes em series temporais : teste de detecção e efeito na previsão de valores agregados

Orientador: Luiz Koodi HottaDissertação (mestrado) - Universidade Estadual de Campinas. Instituto de Matematica, Estatistica e Ciencia da ComputaçãResumo: Neste trabalho são discutidos alguns tipos de valores aberrantes (denotado nesse trabalho por outlier) mais citados na literatura de séries temporais e os efeitos que eles podem causar na identificação, estimação e previsão dos modelos, mostrando assim a importância em detectá-los. Nos primeiros dois capítulos são apresentados os modelos de outliers e alguns testes de detecção existentes na literatura. O Capítulo 3 é dedicado ao estudo dos efeitos dos outliers nas estimações, identificações e previsões. No Capítulo 4 são apresentados os efeitos dos outliers presentes nas últimas observações na previsão de valores agregados, comparando os efeitos nas previsões calculadas através de modelos desagregados e agregados. No estudo são considerados os casos de modelos conhecido e desconhecido, sendo este último realizado através de simulações. De um modo geral, a previsão através de modelo agregado, na presença de outlier aditivo (AO), é menos afetada do que a previsão pelo modelo desagregado. Quando um outlier de inovação (IO) está presente na série a previsão pelo modelo agregado é geralmente mais afetada. Isto era esperado porque no caso de modelos conhecidos o IO não tem efeito nas previsões do modelo desagregado. São também realizados estudos para verificar o efeito dos testes usuais de detecção de outlier na previsão, mostrando que, embora na maioria dos casos a utilização dos testes diminuam os vícios de previsão devido aos outliers, em alguns casos eles aumentam o erro quadrático médio de previsão. Isto ocorre principalmente na presença de dois IOs, de sinais trocados, devido à incorreta detecção dos outliers, na posição e/ou tipo.Abstract: Not informed.MestradoMestre em Estatístic

Tests for linearity in time series: a comparative study.

by Wai-sum Chan.Thesis (M.Ph.)--Chinese University of Hong Kong, 1986Includes bibliographical references

Inspection and Monitoring of Structural Damage Using Vibration Signatures and Smart Techniques

The structural damage detection plays an important role in the evaluation of structural systems and to ensure their safety. Structures like large bridges should be continuously monitored for detection of damage. The cracks usually change the physical parameters like stiffness and flexibility which in turn changes the dynamic properties such as natural frequencies and mode shapes. Crack detection of a beam element comprises of two aspects: the first one is the forward problem which is achieved from the Eigen parameters and the second one is the process to locate and quantify the effect of damage and is termed as ‘inverse process of damage detection’. In the present investigation the analytical and numerical methods are known as the forward problem includes determination of natural frequencies from the knowledge of beam geometry and crack dimension. The vibration signals are derived from the forward problem is exploited in the inverse problem. The natural frequency changes occur due to the various reasons such as boundary condition changes, temperature variations etc. Among all the changes boundary condition changes are the most important factors in structural elements. Many major structures like bridges are made up of uniform beams of unknown boundary conditions. So in the present investigation two of the boundary conditions i.e. fixed -free and fixed- fixed are considered. Using the forward solution method, the natural frequencies are determined. In the inverse solution method various Artificial Intelligence (AI) techniques with their hybrid methods are proposed and implemented. Damage detection problems using Artificial Intelligence techniques require a number of training data sets that represent the uncracked and cracked scenarios of practical structural elements. In the second part of the work different AI techniques like Fuzzy Logic, Genetic Algorithm, Clonal Selection Algorithm, Differential Evolution Algorithm and their hybrid methods are designed and developed. In summary this investigation is a step towards to forecast the position of the damage using the Artificial Intelligence techniques and compare their results. Finally, the results from the Artificial Intelligence techniques and their hybridized algorithms are validated by doing experimental analysis

La végétation aquatique submergée dans les eaux continentales : mieux comprendre sa réponse aux changements environnementaux et ses conséquences sur le fonctionnement des écosystèmes

La végétation aquatique submergée (VAS) est une composante essentielle qui structure les écosystèmes aquatiques continentaux. Elle soutient plusieurs fonctions et services écosystémiques, dont le soutien d’habitats pour la faune, la stabilisation du rivage, le maintien d’une eau claire et la régulation des cycles des nutriments. Cependant, la VAS est soumise aux activités humaines qui modifient leur habitat, altère leur quantité et menacent le maintien de ces services. L’objectif de cette thèse est de mieux comprendre comment les quantités de la VAS répond aux variations environnementales et quels sont les effets de ces modifications sur les fonctions et services qu’elle soutient. Cet objectif est abordé de différents angles d’attaque et à différentes échelles spatiales et temporelles. Tout d’abord, une nouvelle méthode permettant des économies de temps et d’argent pour mesurer la biomasse de la VAS est proposée. À l’aide de deux modèles de calibration, la méthode combine trois techniques existantes couramment utilisées pour estimer la biomasse de la VAS : le prélèvement de biomasse dans des quadrats en plongée, le prélèvement à l’aide d’un râteau manié depuis la surface et l’échosondage à partir d’une embarcation. Cette approche offre l’avantage de limiter l’utilisation risquée et fastidieuse du quadrat avec plongeur, mais fournissant la mesure de biomasse la plus fiable. La première calibration avec le quadrat permet d’utiliser le râteau et corrige son biais, alors que la deuxième calibration entre râteau et échosondage convertit les valeurs mesurées par cette dernière en biomasse. L’utilisation de l’échosondage permet ainsi d’estimer plus rapidement la biomasse à grande échelle. La méthode est validée à partir de données d’échosondage qui sont confrontées avec des biomasses par quadrat, démontrant la robustesse de l’approche. Ensuite, les variations climatiques interannuelles et leurs effets sur la rétention de l’azote ont été évalués pendant six étés dans un herbier aquatique à la confluence de deux tributaires agricoles avec le fleuve Saint-Laurent. Des budgets d’azote journalier ont été estimés par la différence entre les concentrations modélisées de nitrate dans les tributaires et les concentrations sortantes de l’herbier mesurées par une sonde à haute fréquence. La rétention totale a été partitionnée en assimilation autotrophe et en dénitrification à partir de la variation diurne en nitrate. Les budgets ont été confrontés à un indice de biomasse de VAS, la pente de la surface du niveau de l’eau, qui a révélé un portrait détaillé de l’évolution de la biomasse au cours de la saison de croissance. Les résultats montrent que la rétention est influencée par les variations de niveau de l’eau, de température, de biomasse de la VAS et d’apports en nitrate. De hauts taux de consommation de nitrate sont rapportés, parmi les plus élevés mesurés en rivière, avec une biomasse accrue de plantes accrue favorisant l’élimination permanente par la dénitrification. Enfin, une synthèse sur les tendances, les facteurs globaux déterminant les quantité de VAS dans les lacs ainsi que comment les quantité y ont été mesurées est présentée. La compilation a été effectuée à l’aide d’une recherche par mot clés réalisée sur une base de données bibliographiques. La synthèse montre un portrait dynamique dans le temps et dans l’espace des quantités de VAS. Bien que les déclins de quantités soient prédominants, plusieurs séries temporelles récentes indiquent une récupération de la VAS, patrons qui varient selon les régions et les activités humaines. Les usages dans les bassins versants liés à l’eutrophisation sont associés aux déclins, particulièrement en Asie, alors que les augmentations sont surtout associées à la gestion de la VAS en Europe. Les tendances plus variables en Amérique du Nord sont associées à l’arrivée d’espèces envahissantes. Cette thèse innove en fournissant une nouvelle méthode qui facilite la mesure de la biomasse de la VAS à grande échelle. Elle contribue également aux connaissances sur la VAS et le cycle de l’azote en grande rivière en caractérisant la variation de la rétention de nitrate et en soulignant leur important rôle comme site de transformation dans ces écosystèmes. Finalement, elle contribue à la biogéographie de la VAS continentale dans les lacs, indique des lacunes de connaissance, souligne les développements méthodologiques souhaitables et informe sur l’influence de facteurs expliquant la variation de la VAS qui seront utiles pour sa gestion future. Ces informations seront profitables au maintien des fonctions et services soutenus par la VAS et à son utilisation comme une solution fournie par la nature face aux changements globaux.Submerged aquatic vegetation (SAV) is an essential component that structures inland waters. SAV sustains numerous ecosystem services and functions, such as providing habitat for fauna, stabilizing shoreline, maintaining clear water and regulating nutrient cycles. However, SAV is submitted to human activities that modify their habitat, alter their quantities and threaten the ecosystem services they may provide. The objective of this thesis is to better understand how SAV quantities responds to environmental variations, and what are the effect of these modifications on the functions and services they sustain. This objective is approached in different ways and at various spatial and temporal scales. First, a new cost-effective method to measure SAV biomass is proposed. The method combines three existing techniques by means of two calibration models. This approach has the advantage of reducing the hazardous and cumbersome use of quadrats with divers, whilst providing the most accurate biomass measure. The first calibration with the quadrat allows for the application of the rake and corrects for its bias, while the second calibration between rake and echosounding converts the values measured by the latter into biomass. The use of echosounding thus allows for the estimation of biomass more rapidly at larger scale. The method is validated from echosounding data that are compared to quadrat biomasses, demonstrating the robustness of the approach. Second, interannual climate variation and their effect on nitrogen retention were evaluated during six summers in a SAV meadow at the confluence zone of two agricultural tributaries with the Saint Lawrence River. Daily nitrogen budgets were estimated as the difference between modelled nitrate concentration in the tributaries and concentration outflowing the SAV meadow measured with a high frequency sensor. Total retention was partitioned into autotrophic assimilation and denitrification from the diel nitrate variation. The budgets were compared to an indicator of SAV biomass, the slope of water level surface, which provided a detailed portrait of biomass changes throughout the growing season. The results show that retention is influenced by variation in water levels, temperature, SAV biomass and nitrate inputs. Among the highest nitrate uptake rates are reported compared to previous measurements in inland waters, with plant biomass favoring permanent removal through denitrification. Third, a synthesis on trends and drivers of SAV quantities in lakes, as well as on how it was measured is presented. The compilation was conducted from a keyword search on a bibliographic database. The synthesis shows a dynamic depiction in space and time of SAV quantities. Although decreasing quantities are predominant, many recent time series indicate SAV recovery, and these patterns vary with regions and human activities. Direct activities in watersheds leading to eutrophication are associated with decreases, particularly in Asia, while increases are more associated with SAV management in Europe. Trends are more variable in North America due to invasive species. This thesis innovates by providing a new method facilitating SAV biomass measurement at large scale. The thesis also contributes to knowledge on SAV and on nitrogen cycling in large rivers by characterizing the variation in nitrate retention. Finally, the thesis contributes to inland SAV biogeography, identifies knowledge gaps, indicates desirable methodological developments and informs on drivers of SAV that could inform its future management. This information will be beneficial for the preservation of the ecosystem services and functions provided by SAV and its use as a nature-based solution against global changes

Short-term forecast techniques for energy management systems in microgrid applications

A Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of Doctor of Philosophy in Sustainable Energy Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyIn the 2015 Paris Agreement, 195 countries adopted a global climate agreement to limit the global average temperature rise to less than 2°C. Achieving the set targets involves increasing energy efficiency and embracing cleaner energy solutions. Although advances in computing and Internet of Things (IoT) technologies have been made, there is limited scientific research work in this arena that tackles the challenges of implementing low-cost IoT-based Energy Management System (EMS) with energy forecast and user engagement for adoption by a layman both in off-grid or microgrid tied to a weak grid. This study proposes an EMS approach for short-term forecast and monitoring for hybrid microgrids in emerging countries. This is done by addressing typical submodules of EMS namely: load forecast, blackout forecast, and energy monitoring module. A short-term load forecast model framework consisting of a hybrid feature selection and prediction model was developed. Prediction error performance evaluation of the developed model was done by varying input predictors and using the principal subset features to perform supervised training of 20 different conventional prediction models and their hybrid variants. The proposed principal k-features subset union approach registered low error performance values than standard feature selection methods when it was used with the ‘linear Support Vector Machine (SVM)’ prediction model for load forecast. The hybrid regression model formed from a fusion of the best 2 models (‘linearSVM’ and ‘cubicSVM’) showed improved prediction performance than the individual regression models with a reduction in Mean Absolute Error (MAE) by 5.4%. In the case of the EMS blackout prediction aspect, a hybrid Adaptive Similar Day (ASD) and Random Forest (RF) model for short-term power outage prediction was proposed that predicted accurately almost half of the blackouts (49.16%), thereby performing slightly better than the stand-alone RF (32.23%), and ASD (46.57%) models. Additionally, a low-cost EMS smart meter was developed to realize the implemented energy forecast and offer user engagement through monitoring and control of the microgrid towards the goal of increasing energy efficiency