Department of Electrical Engineering

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Now showing 1 - 5 of 90
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    Machine learning-based forecasting of significant daily returns in foreign exchange markets
    (Inderscience Publishers, 2022) Kamalov, Firuz; Gurrib, Ikhlaas
    Financial forecasting has always attracted an enormous amount of interest among researchers in quantitative analysis. The advent of modern machine learning models has introduced new tools to tackle this classical problem. In this paper, we apply machine learning algorithms to a hitherto unexplored question of forecasting instances of significant fluctuations in currency exchange rates. We carry out an extensive comparative study of ten modern machine learning methods. In our experiments, we use data on four major currency pairs over a 20-year period. A key contribution is the novel use of outlier detection methods for this purpose. Numerical experiments show that outlier detection methods substantially outperform traditional machine learning and finance techniques. In addition, we show that a recently proposed new outlier detection method PKDE produces the best overall results. Our findings hold across different currency pairs, significance levels, and time horizons indicating the robustness of the proposed method. Copyright © 2022 Inderscience Enterprises Ltd.
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    Fast Algorithms for Estimating the Disturbance Inception Time in Power Systems Based on Time Series of Instantaneous Values of Current and Voltage with a High Sampling Rate
    (MDPI, 2022-11) Senyuk, Mihail; Beryozkina, Svetlana; Gubin, Pavel; Dmitrieva, Anna; Kamalov, Firuz; Safaraliev, Murodbek; Zicmane, Inga
    The study examines the development and testing of algorithms for disturbance inception time estimation in a power system using instantaneous values of current and voltage with a high sampling rate. The algorithms were tested on both modeled and physical data. The error of signal extremum forecast, the error of signal form forecast, and the signal value at the so-called joint point provided the basis for the suggested algorithms. The method of tuning for each algorithm was described. The time delay and accuracy of the algorithms were evaluated with varying tuning parameters. The algorithms were tested on the two-machine model of a power system in Matlab/Simulink. Signals from emergency event recorders installed on real power facilities were used in testing procedures. The results of this study indicated a possible and promising application of the suggested methods in the emergency control of power systems. © 2022 by the authors.
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    Medium-term forecasting of power generation by hydropower plants in isolated power systems under climate change
    (Elsevier Ltd, 2022-11) Safaraliev, Murodbek; Kiryanova, Natalya; Matrenin, Pavel; Dmitriev, Stepan; Kokin, Sergey; Kamalov, Firuz
    Reliable operation of power systems (PS), including those with a significant share of hydropower plants (HPPs) in the energy balance, largely depends on the accuracy of forecasting power generation. The importance of power generation forecasts increases with the development of renewable power generation, which is stochastic by nature. Those kinds of tasks are complicated by the lack of reliable information on metrological data and estimated energy consumption, which is also stochastic. In the medium-term forecasting (MTF) of power generation by HPPs, the seasonality of changes in flow and inflow of water should be taken into account, which significantly affects the reserves and regulatory capabilities of the power system as a whole. This work discusses the problem of constructing a model for MTF of power generation HPP in isolated power systems (IPS), taking into account such atmospheric parameters as air temperature, wind speed and humidity. To address constant climatic changes, this paper suggests implementing machine learning models. The proposed approach is characterized by a high degree of autonomy and learning automation. The paper provides a comparative study of the machine learning models such as polynomial model with Tikhonov's regularization (LR), k-nearest neighbors (kNN), multilayer perceptron (MLP), ensembles of decision trees, adaptive boosting of linear models (ABLR), etc. Computational experiments have shown that the machine learning approach yields the results of sufficient quality, which allows to use them for forecasting of power generation HPP in isolated power systems under conditions of climate change. The Adaptive Boosting Linear Regression model is the simplest and most reliable machine learning model that has proven itself well in the tasks with a relatively small amount of training samples. © 2022 The Author(s)
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    Deep learning for Covid-19 forecasting: State-of-the-art review
    (Elsevier B.V., 2022-10-28) Kamalov, Firuz; Rajab, Khairan; Cherukuri, Aswani Kumar; Elnagar, Ashraf; Safaraliev, Murodbek
    The Covid-19 pandemic has galvanized scientists to apply machine learning methods to help combat the crisis. Despite the significant amount of research there exists no comprehensive survey devoted specifically to examining deep learning methods for Covid-19 forecasting. In this paper, we fill the gap in the literature by reviewing and analyzing the current studies that use deep learning for Covid-19 forecasting. In our review, all published papers and preprints, discoverable through Google Scholar, for the period from Apr 1, 2020 to Feb 20, 2022 which describe deep learning approaches to forecasting Covid-19 were considered. Our search identified 152 studies, of which 53 passed the initial quality screening and were included in our survey. We propose a model-based taxonomy to categorize the literature. We describe each model and highlight its performance. Finally, the deficiencies of the existing approaches are identified and the necessary improvements for future research are elucidated. The study provides a gateway for researchers who are interested in forecasting Covid-19 using deep learning. © 2022 Elsevier B.V.
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    A computational numerical performance for solving the mathematical epidemiological model based on influenza disease
    (Elsevier B.V., 2022-09) Jain, Sonal; Leung, Ho-Hon; Kamalov, Firuz
    Understanding epidemic propagation patterns and assessing disease control measures require the use of mathematical and computational methodologies. In recent years, complexity science, management science, sociology, and computer science have all been progressively merged with epidemiology. The interdisciplinary collaboration has sped up the development of computational and mathematical methods for simulating epidemics. The model with the classical time derivative in the influenza disease model is formulated with the Caputo (power-law kernel), Caputo–Fabrizio (exponential kernel), and the novel Atangana–Baleanu fractional derivatives which combined both nonlocal and non-singular properties. Also this article presents the boundness and positiveness Solutions for the influenza model. The analysis of the equilibrium point is also given. Various published articles have utilized the reproductive number notion to investigate disease-spread stability. There were certain conditions proposed to predict whether there would be stability or instability. It was also advised that an analysis be conducted to discover the conditions under which infectious classes will grow or die out. Some authors pointed out that the reproductive number is limited, including its inability to fairly aid in understanding distribution patterns. The concept of strength number and analysis of derivatives of mathematical models were presented to help in understanding the disease model. Further, the stability of disease-free and endemic equilibrium is presented. Finally, a numerical solution with simulation is given. We hope to use these extra studies in a basic model to forecast the future of this research. © 2022 The Author(s)