Regime Switching Models for Electricity Time Series that Capture Fat Tailed Distributions

In the power market, electricity prices play an important role at the economic level. The behavior of a price trend usually known as a structural break may change over time in terms of its mean value, its volatility, or it may change for a period of time before reverting back to its original behavior or switching to another style of behavior, and the latter is typically termed a regime shift or regime switch. Our task in this thesis is to develop an electricity price time series model that captures fat tailed distributions which can explain this behavior and analyze it for better understanding. For NordPool data used, the obtained Markov Regime-Switching model operates on two regimes: regular and non-regular. Three criteria have been considered price difference criterion, capacity/flow difference criterion and spikes in Finland criterion. The suitability of GARCH modeling to simulate multi-regime modeling is also studied.

Learning Robot Motions from Visual Sensing

Learning from demonstration becomes increasingly popular as an efficient way of robot programming. Not only a scientific interest acts as an inspiration in this case but also the possibility of producing the machines that would find application in different areas of life: robots helping with daily routine at home, high performance automata in industries or friendly toys for children. One way to teach a robot to fulfill complex tasks is to start with simple training exercises, combining them to form more difficult behavior. The objective of the Master’s thesis work was to study robot programming with visual input. Dynamic movement primitives (DMPs) were chosen as a tool for motion learning and generation. Assuming a movement to be a spring system influenced by an external force, making this system move, DMPs represent the motion as a set of non-linear differential equations. During the experiments the properties of DMP, such as temporal and spacial invariance, were examined. The effect of the DMP parameters, including spring coefficient, damping factor, temporal scaling, on the trajectory generated were studied.

Uusiutuvat kvasikonformikuvaukset

Kirjallisuusarvostelu

On non-ideal and non-linear portal frame dynamics analysis using bogoliubov averaging method

We apply the Bogoliubov Averaging Method to the study of the vibrations of an elastic foundation, forced by a Non-ideal energy source. The considered model consists of a portal plane frame with quadratic nonlinearities, with internal resonance 1:2, supporting a direct current motor with limited power. The non-ideal excitation is in primary resonance in the order of one-half with the second mode frequency. The results of the averaging method, plotted in time evolution curve and phase diagrams are compared to those obtained by numerically integrating of the original differential equations. The presence of the saturation phenomenon is verified by analytical procedures.

On state and parameter estimation in chaotic systems

State-of-the-art predictions of atmospheric states rely on large-scale numerical models of chaotic systems. This dissertation studies numerical methods for state and parameter estimation in such systems. The motivation comes from weather and climate models and a methodological perspective is adopted. The dissertation comprises three sections: state estimation, parameter estimation and chemical data assimilation with real atmospheric satellite data. In the state estimation part of this dissertation, a new filtering technique based on a combination of ensemble and variational Kalman filtering approaches, is presented, experimented and discussed. This new filter is developed for large-scale Kalman filtering applications. In the parameter estimation part, three different techniques for parameter estimation in chaotic systems are considered. The methods are studied using the parameterized Lorenz 95 system, which is a benchmark model for data assimilation. In addition, a dilemma related to the uniqueness of weather and climate model closure parameters is discussed. In the data-oriented part of this dissertation, data from the Global Ozone Monitoring by Occultation of Stars (GOMOS) satellite instrument are considered and an alternative algorithm to retrieve atmospheric parameters from the measurements is presented. The validation study presents first global comparisons between two unique satellite-borne datasets of vertical profiles of nitrogen trioxide (NO3), retrieved using GOMOS and Stratospheric Aerosol and Gas Experiment III (SAGE III) satellite instruments. The GOMOS NO3 observations are also considered in a chemical state estimation study in order to retrieve stratospheric temperature profiles. The main result of this dissertation is the consideration of likelihood calculations via Kalman filtering outputs. The concept has previously been used together with stochastic differential equations and in time series analysis. In this work, the concept is applied to chaotic dynamical systems and used together with Markov chain Monte Carlo (MCMC) methods for statistical analysis. In particular, this methodology is advocated for use in numerical weather prediction (NWP) and climate model applications. In addition, the concept is shown to be useful in estimating the filter-specific parameters related, e.g., to model error covariance matrix parameters.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Determining vehicle drivetrain dynamics using system identification

Target of this book is to propose an approach for modelling drivetrain dynamics in order to design further a vibration control system of a hybrid bus. In this thesis two approaches are examined and compared. First model is obtained by theoretical means: drivetrain is represented as a system of rotating masses, which motion is described with differential equations. Second model is obtained using system identification method: mathematical description of the dynamic behavior of a system is formed based on measured input (torque) and output (speed) data. Then two models are compared and an optimal approach is suggested.

Multicomponent diffusion during Prato cheese ripening: mathematical modeling using the finite element method

The partial replacement of NaCl by KCl is a promising alternative to produce a cheese with lower sodium content since KCl does not change the final quality of the cheese product. In order to assure proper salt proportions, mathematical models are employed to control the product process and simulate the multicomponent diffusion during the reduced salt cheese ripening period. The generalized Fick's Second Law is widely accepted as the primary mass transfer model within solid foods. The Finite Element Method (FEM) was used to solve the system of differential equations formed. Therefore, a NaCl and KCl multicomponent diffusion was simulated using a 20% (w/w) static brine with 70% NaCl and 30% KCl during Prato cheese (a Brazilian semi-hard cheese) salting and ripening. The theoretical results were compared with experimental data, and indicated that the deviation was 4.43% for NaCl and 4.72% for KCl validating the proposed model for the production of good quality, reduced-sodium cheeses.

Mathematical modeling of microbial growth in milk

A mathematical model to predict microbial growth in milk was developed and analyzed. The model consists of a system of two differential equations of first order. The equations are based on physical hypotheses of population growth. The model was applied to five different sets of data of microbial growth in dairy products selected from Combase, which is the most important database in the area with thousands of datasets from around the world, and the results showed a good fit. In addition, the model provides equations for the evaluation of the maximum specific growth rate and the duration of the lag phase which may provide useful information about microbial growth.

Neopentyyliglykolin ja propionihapon välisen esteröintireaktion kinetiikka

Glykolien esterit ovat haluttuja pintareaktiivisia aineita. Niitä voidaan valmistaa esteröintireaktiolla karboksyylihappojen kanssa katalyytin läsnä ollessa, jolloin toivottu reaktiotuote on yleensä muodostuva monoesteri. Monoesterin saannon lisäämiseksi reaktiossa muodostuvaa vettä voidaan poistaa jatkuvasti reaktiosta. Reaktion tasapainotilan tutkiminen on kuitenkin tärkeää, jotta reaktion kinetiikka tunnettaisiin mahdollisimman hyvin. Tällöin reaktiotuotteita ei poisteta reaktioseoksesta reaktion aikana. Glykolit esteröityvät happojen kanssa kahdessa vaiheessa. Ensimmäisessä vaiheessa muodostuu monoesteriä ja vettä ja toisessa vaiheessa diesteriä ja vettä. Kokeiden perusteella ensimmäinen vaihe on selvästi toista vaihetta nopeampi reaktio. Kirjallisuudessa on esitetty myös kaksi sivureaktiota, transesteröityminen ja disproportionaatio. Reaktion kinetiikka voidaan kuvata ilman näitä pieniä sivureaktiota, mutta täydellisen kuvaamisen vuoksi on ne myös otettava huomioon. Reaktion kinetiikan tutkimiseksi suoritettiin viisi laboratoriokoetta eri lämpötiloissa neopentyyliglykolilla ja propionihapolla homogeenisen para-tolueenisulfonihapon toimiessa katalyyttina. Lähtöaineiden ja tuotteiden konsentraatioita seurattiin ajan funktiona ja saatujen tulosten perusteella sovitettiin reaktiomekanismin differentiaaliyhtälöiden reaktionopeusvakiot. Nopeusvakioiden lämpötilariippuvuutta tutkittiin Arrheniuksen yhtälön avulla. Lisäksi määritettiin tasapainovakiot kullekin osareaktiolle.

Stochastic particle models: mean reversion and burgers dynamics. An application to commodity markets

The aim of this study is to propose a stochastic model for commodity markets linked with the Burgers equation from fluid dynamics. We construct a stochastic particles method for commodity markets, in which particles represent market participants. A discontinuity in the model is included through an interacting kernel equal to the Heaviside function and its link with the Burgers equation is given. The Burgers equation and the connection of this model with stochastic differential equations are also studied. Further, based on the law of large numbers, we prove the convergence, for large N, of a system of stochastic differential equations describing the evolution of the prices of N traders to a deterministic partial differential equation of Burgers type. Numerical experiments highlight the success of the new proposal in modeling some commodity markets, and this is confirmed by the ability of the model to reproduce price spikes when their effects occur in a sufficiently long period of time.

Sampling Interval and estimated Betas : Implications for the Presence of Transitory Components in Stock Prices

We provide a theoretical framework to explain the empirical finding that the estimated betas are sensitive to the sampling interval even when using continuously compounded returns. We suppose that stock prices have both permanent and transitory components. The permanent component is a standard geometric Brownian motion while the transitory component is a stationary Ornstein-Uhlenbeck process. The discrete time representation of the beta depends on the sampling interval and two components labelled \"permanent and transitory betas\". We show that if no transitory component is present in stock prices, then no sampling interval effect occurs. However, the presence of a transitory component implies that the beta is an increasing (decreasing) function of the sampling interval for more (less) risky assets. In our framework, assets are labelled risky if their \"permanent beta\" is greater than their \"transitory beta\" and vice versa for less risky assets. Simulations show that our theoretical results provide good approximations for the means and standard deviations of estimated betas in small samples. Our results can be perceived as indirect evidence for the presence of a transitory component in stock prices, as proposed by Fama and French (1988) and Poterba and Summers (1988).

Anisotropie de la photoluminescence dans des nanostructures organiques chirales autoassemblées

Nous investiguons dans ce travail la dynamique des excitons dans une couche mince d’agrégats H autoassemblés hélicoïdaux de molécules de sexithiophène. Le couplage intermoléculaire (J=100 meV) place ce matériau dans la catégorie des semi-conducteurs à couplage de type intermédiaire. Le désordre énergétique et la forte interaction électronsphonons causent une forte localisation des excitons. Les espèces initiales se ramifient en deux états distincts : un état d’excitons autopiégés (rendement de 95 %) et un état à transfert de charge (rendement de 5%). À température de la pièce (293K), les processus de sauts intermoléculaires sont activés et l’anisotropie de la fluorescence décroît rapidement à zéro en 5 ns. À basse température (14K), les processus de sauts sont gelés. Pour caractériser la dynamique de diffusion des espèces, une expérience d’anisotropie de fluorescence a été effectuée. Celle-ci consiste à mesurer la différence entre la photoluminescence polarisée parallèlement au laser excitateur et celle polarisée perpendiculairement, en fonction du temps. Cette mesure nous donne de l’information sur la dépolarisation des excitons, qui est directement reliée à leur diffusion dans la structure supramoléculaire. On mesure une anisotropie de 0,1 après 20 ns qui perdure jusqu’à 50ns. Les états à transfert de charge causent une remontée de l’anisotropie vers une valeur de 0,15 sur une plage temporelle allant de 50 ns jusqu’à 210 ns (période entre les impulsions laser). Ces résultats démontrent que la localisation des porteurs est très grande à 14K, et qu’elle est supérieure pour les espèces à transfert de charge. Un modèle numérique simple d’équations différentielles à temps de vie radiatif et de dépolarisation constants permet de reproduire les données expérimentales. Ce modèle a toutefois ses limitations, notamment en ce qui a trait aux mécanismes de dépolarisation des excitons.

Simulation numérique de feux de forêt avec réinitialisation et contournement d’obstacles

Ce travail présente une technique de simulation de feux de forêt qui utilise la méthode Level-Set. On utilise une équation aux dérivées partielles pour déformer une surface sur laquelle est imbriqué notre front de flamme. Les bases mathématiques de la méthode Level-set sont présentées. On explique ensuite une méthode de réinitialisation permettant de traiter de manière robuste des données réelles et de diminuer le temps de calcul. On étudie ensuite l’effet de la présence d’obstacles dans le domaine de propagation du feu. Finalement, la question de la recherche du point d’ignition d’un incendie est abordée.

Solutions de rang k et invariants de Riemann pour les systèmes de type hydrodynamique multidimensionnels

Dans ce travail, nous adaptons la méthode des symétries conditionnelles afin de construire des solutions exprimées en termes des invariants de Riemann. Dans ce contexte, nous considérons des systèmes non elliptiques quasilinéaires homogènes (de type hydrodynamique) du premier ordre d'équations aux dérivées partielles multidimensionnelles. Nous décrivons en détail les conditions nécessaires et suffisantes pour garantir l'existence locale de ce type de solution. Nous étudions les relations entre la structure des éléments intégraux et la possibilité de construire certaines classes de solutions de rang k. Ces classes de solutions incluent les superpositions non linéaires d'ondes de Riemann ainsi que les solutions multisolitoniques. Nous généralisons cette méthode aux systèmes non homogènes quasilinéaires et non elliptiques du premier ordre. Ces méthodes sont appliquées aux équations de la dynamique des fluides en (3+1) dimensions modélisant le flot d'un fluide isentropique. De nouvelles classes de solutions de rang 2 et 3 sont construites et elles incluent des solutions double- et triple-solitoniques. De nouveaux phénomènes non linéaires et linéaires sont établis pour la superposition des ondes de Riemann. Finalement, nous discutons de certains aspects concernant la construction de solutions de rang 2 pour l'équation de Kadomtsev-Petviashvili sans dispersion.