Uncovering predictability in the evolution of the WTI oil futures curve

Accurately forecasting the price of oil, the world's most actively traded commodity, is of great importance to both academics and practitioners. We contribute by proposing a functional time series based method to model and forecast oil futures. Our approach boasts a number of theoretical and practical advantages including effectively exploiting underlying process dynamics missed by classical discrete approaches. We evaluate the finite-sample performance against established benchmarks using a model confidence set test. A realistic out-of-sample exercise provides strong support for the adoption of our approach with it residing in the superior set of models in all considered instances.Comment: 28 pages, 4 figures, to appear in European Financial Managemen

Applications of functional data analysis : A systematic review

Background Functional data analysis (FDA) is increasingly being used to better analyze, model and predict time series data. Key aspects of FDA include the choice of smoothing technique, data reduction, adjustment for clustering, functional linear modeling and forecasting methods. Methods A systematic review using 11 electronic databases was conducted to identify FDA application studies published in the peer-review literature during 1995–2010. Papers reporting methodological considerations only were excluded, as were non-English articles. Results In total, 84 FDA application articles were identified; 75.0% of the reviewed articles have been published since 2005. Application of FDA has appeared in a large number of publications across various fields of sciences; the majority is related to biomedicine applications (21.4%). Overall, 72 studies (85.7%) provided information about the type of smoothing techniques used, with B-spline smoothing (29.8%) being the most popular. Functional principal component analysis (FPCA) for extracting information from functional data was reported in 51 (60.7%) studies. One-quarter (25.0%) of the published studies used functional linear models to describe relationships between explanatory and outcome variables and only 8.3% used FDA for forecasting time series data. Conclusions Despite its clear benefits for analyzing time series data, full appreciation of the key features and value of FDA have been limited to date, though the applications show its relevance to many public health and biomedical problems. Wider application of FDA to all studies involving correlated measurements should allow better modeling of, and predictions from, such data in the future especially as FDA makes no a priori age and time effects assumptions

Evaluation of ATM Cash Demand Process Factors Applied for Forecasting with CI Models

The purpose of cash management is to optimize distribution of cash. Effective cash management brings savings to retail banks that are related to: dormant cash reduction; reduced replenishment costs; decrease of cash preparation costs; reduction of cash insurance costs. Optimization of cash distribution for retail banking in ATM and branch networks requires estimation of cash demand/supply in the future. This estimation determines overall cash management efficiency: accurate cash demand estimation reduces bank overall costs. In order to estimate cash demand in the future, cash flow forecasting must be performed that is usually based on historical cash point (ATM or branch) cash flow data. Many factors that are uncertain and may change in time influence cash supply/demand process for cash point. These may change throughout cash points and are related to location, climate, holiday, celebration day and special event (such as salary days and sale of nearby supermarket) factors. Some factors affect cash demand periodically. Periodical factors form various seasonality in cash flow process: daily (related to intraday factors throughout the day), weekly (mostly related to weekend effects), monthly (related to payday) and yearly (related to climate seasons, tourist and student arrivals, periodical celebration days such as New Year) seasons. Uncertain (aperiodic) factors are mostly related to celebration days that do not occur periodically (such as Easter), structural break factors that form long term or permanent cash flow shift (new shopping mall near cash point, shift of working hours) and some may be temporal (reconstruction of nearby building that restricts cash point reachability). Those factors form cash flow process that contains linear or nonlinear trend, mixtures of various seasonal components (intraday, weekly, monthly yearly), level shifts and heteroscedastic uncertainty. So historical data-based forecasting models need to be able to approximate historical cash demand process as accurately as possible properly evaluating these factors and perform forecasting of cash flow in the future based on estimated empirical relationship.</p

Minimax estimation of the mode of functional data

Wir untersuchen den Modalwert einer Verteilung, die auf einem Funktionenraum wie etwa dem Raum integrierbarer Funktionen definiert ist. Die Definition des Modalwerts basiert auf Small-Ball-Wahrscheinlichkeiten. Wir benutzen Entropiemethoden wie etwa endliche Überdeckungen für die Definition eines Modalwertschätzers und die Beschreibung seines asymptotischen Verhaltens. Wir zeigen die starke Konsistenz und ermitteln die optimale Konvergenzrate für eine Klasse von Verteilungen, deren Modalwerte in einer totalbeschränkten Teilmenge des Funktionenraums liegen.We investigate the mode of a distribution defined on a function space, e.g. the space of integrable functions. We give a definition of the mode using small ball probabilities. We use entropy methods, e.g. finite covers, to define an estimator of the mode and to deduce its asymptotic behaviour. We show strong consistency and continue to derive the optimal rate of convergence over a class of distributions whose modes are contained in a totally bounded subset of the function space

Caractérisation du comportement de systèmes électriques aéronautiques à partir d'analyses statistiques

La caractérisation des systèmes électriques est une tâche essentielle dans la conception aéronautique. Elle consiste notamment à dimensionner les composants des systèmes, définir les exigences à respecter par les charges électriques, définir les intervalles de maintenance et identifier les causes racines des pannes sur avions. Aujourd'hui, les calculs sont basés sur la théorie du génie électrique ou des modèles physiques simulés. L'objectif de cette thèse est d'utiliser une approche statistique basée sur les données observées durant les vols et des modèles d'apprentissage automatique pour caractériser le comportement du système électrique aéronautique. La première partie de cette thèse traite de l'estimation de la consommation électrique maximale que fournit un système électrique, dans le but d'optimiser le dimensionnement des générateurs et de mieux connaître les marges réelles. La théorie des valeurs extrêmes a été utilisée pour estimer des quantiles qui sont comparés aux valeurs théoriques calculées par les ingénieurs. Dans la deuxième partie, différents modèles régularisés sont considérés pour prédire la température de l'huile du générateur électrique dans un contexte de données fonctionnelles. Cette étude permet notamment de comprendre le comportement du générateur dans des conditions extrêmes qui ne peuvent pas être réalisées physiquement. Enfin, dans la dernière partie, un modèle de maintenance prédictive est proposé afin de détecter des anomalies dans le fonctionnement du générateur électrique pour anticiper les pannes. Le modèle proposé utilise des variantes de la méthode "Invariant Coordinate Selection" pour des données fonctionnelles.The characterization of electrical systems is an essential task in aeronautic conception. It consists in particular of sizing the electrical components, defining maintenance frequency and finding the root cause of aircraft failures. Nowadays, the computations are made using electrical engineering theory and simulated physical models. The aim of this thesis is to use statistical approaches based on flight data and machine learning models to characterize the behavior of aeronautic electrical systems. In the first part, we estimate the maximal electrical consumption that the generator should deliver to optimize the generator size and to better understand its real margin. Using the extreme value theory we estimate quantiles that we compare to the theoretical values computed by the electrical engineers. In the second part, we compare different regularized procedures to predict the oil temperature of a generator in a functional data framework. In particular, this study makes it possible to understand the generator behavior under extreme conditions that could not be reproduced physically. Finally, in the last part, we develop a predictive maintenance model that detects the abnormal behavior of a generator to anticipate failures. This model is based on variants of "Invariant Coordinate Selection" adapted to functional data