Modélisation d'une série financière par mouvement Brownien multi-fractionnaire parcimonieux

International audienceDans ce travail, nous introduisons un modèle parcimonieux de processus de type fractal. En premier, nous rappelons le passage du mouvement brownien fractionnaire (fBm) au mouvement brownien multi-fractionnaire (mBm) et proposons de sélectionner un modèle parcimonieux. En second, nous étayons notre point de vue par des expériences statistiques et l'observation d'un artefact numérique: quand nous estimons l'index de Hurst dépendant du temps H(t) pour un mouvement brownien fractionnaire, la fluctuation de l'échantillonnage donne l'impression que H(t) est lui-même un processus stochastique, même lorsque H(t) est constant. Nous appliquons cette modélisation à des données financières réelles: la série des prix journaliers du Nasdaq de 1971 jusqu'à la fin 2009

Modélisation d'une série financière par mouvement Brownien multi-fractionnaire parcimonieux

International audienceDans ce travail, nous introduisons un modèle parcimonieux de processus de type fractal. En premier, nous rappelons le passage du mouvement brownien fractionnaire (fBm) au mouvement brownien multi-fractionnaire (mBm) et proposons de sélectionner un modèle parcimonieux. En second, nous étayons notre point de vue par des expériences statistiques et l'observation d'un artefact numérique: quand nous estimons l'index de Hurst dépendant du temps H(t) pour un mouvement brownien fractionnaire, la fluctuation de l'échantillonnage donne l'impression que H(t) est lui-même un processus stochastique, même lorsque H(t) est constant. Nous appliquons cette modélisation à des données financières réelles: la série des prix journaliers du Nasdaq de 1971 jusqu'à la fin 2009

Short-Term Forecasting of Photovoltaic Solar Power Production Using Variational Auto-Encoder Driven Deep Learning Approach

The accurate modeling and forecasting of the power output of photovoltaic (PV) systems are critical to efficiently managing their integration in smart grids, delivery, and storage. This paper intends to provide efficient short-term forecasting of solar power production using Variational AutoEncoder (VAE) model. Adopting the VAE-driven deep learning model is expected to improve forecasting accuracy because of its suitable performance in time-series modeling and flexible nonlinear approximation. Both single- and multi-step-ahead forecasts are investigated in this work. Data from two grid-connected plants (a 243 kW parking lot canopy array in the US and a 9 MW PV system in Algeria) are employed to show the investigated deep learning models’ performance. Specifically, the forecasting outputs of the proposed VAE-based forecasting method have been compared with seven deep learning methods, namely recurrent neural network, Long short-term memory (LSTM), Bidirectional LSTM, Convolutional LSTM network, Gated recurrent units, stacked autoencoder, and restricted Boltzmann machine, and two commonly used machine learning methods, namely logistic regression and support vector regression. The results of this investigation demonstrate the satisfying performance of deep learning techniques to forecast solar power and point out that the VAE consistently performed better than the other methods. Also, results confirmed the superior performance of deep learning models compared to the two considered baseline machine learning models

Ensemble Learning Techniques-Based Monitoring Charts for Fault Detection in Photovoltaic Systems

Over the past few years, there has been a significant increase in the interest in and adoption of solar energy all over the world. However, despite ongoing efforts to protect photovoltaic (PV) plants, they are continuously exposed to numerous anomalies. If not detected accurately and in a timely manner, anomalies in PV plants may degrade the desired performance and result in severe consequences. Hence, developing effective and flexible methods capable of early detection of anomalies in PV plants is essential for enhancing their management. This paper proposes flexible data-driven techniques to accurately detect anomalies in the DC side of the PV plants. Essentially, this approach amalgamates the desirable characteristics of ensemble learning approaches (i.e., the boosting (BS) and bagging (BG)) and the sensitivity of the Double Exponentially Weighted Moving Average (DEWMA) chart. Here, we employ ensemble learning techniques to exploit their capability to enhance the modeling accuracy and the sensitivity of the DEWMA monitoring chart to uncover potential anomalies. In the ensemble models, the values of parameters are selected with the assistance of the Bayesian optimization algorithm. Here, BS and BG are adopted to obtain residuals, which are then monitored by the DEWMA chart. Kernel density estimation is utilized to define the decision thresholds of the proposed ensemble learning-based charts. The proposed monitoring schemes are illustrated via actual measurements from a 9.54 kW PV plant. Results showed the superior detection performance of the BS and BG-based DEWMA charts with non-parametric threshold in uncovering different types of anomalies, including circuit breaker faults, inverter disconnections, and short-circuit faults. In addition, the performance of the proposed schemes is compared to that of BG and BS-based DEWMA and EWMA charts with parametric thresholds

<i>Mentha pulegium</i> extract as a natural product for the inhibition of corrosion. Part I: electrochemical studies

<div><p>The inhibitory effect of <i>Mentha pulegium</i> extract (MPE) on steel corrosion in 1 M HCl solution was investigated using potentiodynamic polarisation and electrochemical impedance spectroscopy. The inhibition efficiency of MPE was found to increase with the concentration and reached 88% at 33% (v/v). Polarisation measurements show that the natural extract acted as a mixed inhibitor. The remarkable inhibition efficiency of MPE was discussed in terms of blocking of electrode surface by adsorption of inhibitor molecules through active centres. The adsorption of MPE was found to accord with the Temkin isotherm.</p></div

Supplementary Material for: Thrombolysis for acute wake-up and unclear onset strokes with alteplase at 0.6 mg/kg in clinical practice: THAWS2 Study

Introduction: The aim of this study was to determine the safety and efficacy of intravenous (IV) alteplase at 0.6 mg/kg for patients with acute wake-up or unclear onset strokes in clinical practice. Methods: This multicenter observational study enrolled acute ischemic stroke patients with last-known-well time >4.5 h who had mismatch between DWI and FLAIR and were treated with IV alteplase. The safety outcomes were symptomatic intracranial hemorrhage (sICH) after thrombolysis, all-cause deaths and all adverse events. The efficacy outcomes were favorable outcome defined as an mRS score of 0–1 or recovery to the same mRS score as the premorbid score, complete independence defined as an mRS score of 0–1 at 90 days, and change in NIHSS at 24 h from baseline. Results: Sixty-six patients (35 females; mean age, 74±11 years; premorbid complete independence, 54 [82%]; median NIHSS on admission, 11) were enrolled at 15 hospitals. Two patients (3%) had sICH. Median NIHSS changed from 11 (IQR, 6.75–16.25) at baseline to 5 (3–12.25) at 24 h after alteplase initiation (change, –4.8±8.1). At discharge, 31 patients (47%) had favorable outcome and 29 (44%) had complete independence. None died within 90 days. Twenty-three (35%) also underwent mechanical thrombectomy (no sICH, NIHSS change of –8.5±7.3), of whom 11 (48%) were completely independent at discharge. Conclusions: In real-world clinical practice, IV alteplase for unclear onset stroke patients with DWI-FLAIR mismatch provided safe and efficacious outcomes comparable to those in previous trials. Additional mechanical thrombectomy was performed safely in them