Prévision

La prévision est une . D'une façon générale, . Dans un sens plus restrictif, en épistémologie contemporaine, la prévision se distingue de la prédiction, qui est issue d'une loi ou théorie scientifique hautement confirmée ou corroborée, tandis que la prévision découle d'hypothèses ou de conjectures moins assurées. Prévisibilité et prédictibilité La prévisibilité et la prédictibilité désignent la possibilité que certains événements ou phénomènes soient prévus ou prédits à partir d'une hypothèse ou d'une théorie scientifique et de conditions initiales appropriées. Le philosophe des sciences Karl Popper a bien mis en évidence les limites que posent de nombreux obstacles à la capacité de la science à prédire l'évolution des phénomènes : manque de prévisibilité en raison de facteurs non déterministes (hasard, loi probabiliste, contingence), d'hypothèses peu assurées, d'ignorance de la loi d'évolution ; manque de prédictibilité en raison de l'imprécision des paramètres (notamm

Joint Fusion Learning of Multiple Time Series Prediction

Orçun Gümüs

Accurate traffic density estimations is essential for numerous purposes like the developing successful transit policies or to forecast future traffic conditions for navigation. Current developments in the machine learning and computer systems bring the transportation industry numerous possibilities to improve their operations using data analyses on traffic flow sensor data . However, even state-of-art algorithms for time series forecasting perform well on some transportation problems, they still fail to solve some critical tasks. In particular, existing traffic flow forecasting methods that are not utilising causality relations between different data sources are still unsatisfying for many real-world applications . In this report, we have focused on a new method named joint fusion learning that uses underlying causality in time series. We test our method in a very detailed synthetic environment that we specially developed to imitate real-world traffic flow dataset. In the end, we use our joint-fusion learning on a historical traffic flow dataset for Thessaloniki, Greece which is published by Hellenic Institute of Transport (HIT) . We obtained better results on the short-term forecasts compared the widely-used benchmarks models that uses single time series to forecast the future.

2019

Les mathématiques financières robustes, le maritime et les FFA

Jérôme Reboulleau

Powerful mathematical tools have been developed for trading in stocks and bonds, but other markets that are equally important for the globalized world have to some extent been neglected. We decided to study the shipping market as an new area of development in mathematical finance. The market in shipping derivatives (FFA and FOSVA) has only been developed after 2000 and now exhibits impressive growth. Financial actors have entered the field, but it is still largely undiscovered by institutional investors. The first part of the work was to identify the characteristics of the market in shipping, i.e. the segmentation and the volatility. Because the shipping business is old-fashioned, even the leading actors on the world stage (ship owners and banks) are using macro-economic models to forecast the rates. If the macro-economic models are logical and make sense, they fail to predict. For example, the factor port congestion has been much cited during the last few years, but it is clearly very difficult to control and is simply an indicator of traffic. From our own experience it appears that most ship owners are in fact market driven and rather bad at anticipating trends. Due to their ability to capture large moves, we chose to consider Lévy processes for the underlying price process. Compared with the macro-economic approach, the main advantage is the uniform and systematic structure this imposed on the models. We get in each case a favorable result for our technology and a gain in forecasting accuracy of around 10% depending on the maturity. The global distribution is more effectively modelled and the tails of the distribution are particularly well represented. This model can be used to forecast the market but also to evaluate the risk, for example, by computing the VaR. An important limitation is the non-robustness in the estimation of the Lévy processes. The use of robust estimators reinforces the information obtained from the observed data. Because maximum likelihood estimation is not easy to compute with complex processes, we only consider some very general robust score functions to manage the technical problems. Two new class of robust estimators are suggested. These are based on the work of F. Hampel ([29]) and P. Huber ([30]) using influence functions. The main idea is to bound the maximum likelihood score function. By doing this a bias is created in the parameters estimation, which can be corrected by using a modification of the following type and as proposed by F. Hampel. The procedure for finding a robust estimating equation is thus decomposed into two consecutive steps : Subtract the bias correction and then Bound the score function. In the case of complex Lévy processes, the bias correction is difficult to compute and generally unknown. We have developed a pragmatic solution by inverting the Hampel's procedure. Bound the score function and then Correct for the bias. The price is a loss of the theoretical properties of our estimators, besides the procedure converges to maximum likelihood estimate. A second solution to for achieving robust estimation is presented. It considers the limiting case when the upper and lower bounds tend to zero and leads to B-robust estimators. Because of the complexity of the Lévy distributions, this leads to identification problems.

EPFL2008

Les mathématiques financières robustes, le maritime et les FFA

Jérôme Reboulleau

EPFL2008

Joint Fusion Learning of Multiple Time Series Prediction

Demand forecast for pharmaceutical company

Les mathématiques financières robustes, le maritime et les FFA

Les mathématiques financières robustes, le maritime et les FFA

Joint Fusion Learning of Multiple Time Series Prediction

Demand forecast for pharmaceutical company