1 research outputs found
Financial time series analysis with competitive neural networks
Lāobjectif principal de meĢmoire est la modeĢlisation des donneĢes temporelles non stationnaires. Bien que les modeĢles statistiques classiques tentent de corriger les donneĢes non stationnaires en diffeĢrenciant et en ajustant pour la tendance, je tente de creĢer des grappes localiseĢes de donneĢes de seĢries temporelles stationnaires graĢce aĢ lāalgorithme du Ā« self-organizing map Ā». Bien que de nombreuses techniques aient eĢteĢ deĢveloppeĢes pour les seĢries chronologiques aĢ lāaide du Ā« self- organizing map Ā», je tente de construire un cadre matheĢmatique qui justifie son utilisation dans la preĢvision des seĢries chronologiques financieĢres. De plus, je compare les meĢthodes de preĢvision existantes aĢ lāaide du SOM avec celles pour lesquelles un cadre matheĢmatique a eĢteĢ deĢveloppeĢ et qui nāont pas eĢteĢ appliqueĢes dans un contexte de preĢvision. Je compare ces meĢthodes avec la meĢthode ARIMA bien connue pour la preĢvision des seĢries chronologiques. Le deuxieĢme objectif de meĢmoire est de deĢmontrer la capaciteĢ du Ā« self-organizing map Ā» aĢ regrouper des donneĢes vectorielles, puisquāelle a eĢteĢ deĢveloppeĢe aĢ lāorigine comme un reĢseau neuronal avec lāobjectif de regroupement. Plus preĢciseĢment, je deĢmontrerai ses capaciteĢs de regroupement sur les donneĢes du Ā« limit order book Ā» et preĢsenterai diverses meĢthodes de visualisation de ses sorties.The main objective of this Masterās 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 mapās 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