Electric vehicle (EV) charging demand introduces significant variability in power systems, requiring forecasting approaches capable of representing both aggregated consumption trends and stochastic charging behaviors. While machine learning methods o...
In this work, we study the possibility of using a new non-homogeneous stochastic diffusion process based on the Rayleigh density function to model the evolution of the active cases of COVID-19 in Morocco. First, the main probabilistic characteristics...
In this study, three model perturbation schemes, the stochastically perturbed parameter scheme (SPP), stochastically perturbed physics tendency (SPPT), and multi-physics process parameterization (MP), were used to represent the model errors in the re...
The distribution laws of various natural and anthropogenic processes in the world around us are stochastic in nature. The development of mathematics and, in particular, of stochastic modeling allows us to study regularities in such processes. In prac...
This paper examines the relationship between dependence and independence alternatives in general stochastic processes and explores the duality between the true (yet unknown) stochastic process and the functional representation that fits the observed...
Functional magnetic resonance imaging (fMRI) signals exhibit complex temporal structure arising from multivariate neural dynamics, physiological variability, and measurement uncertainty. In this work, we formulate region-of-interest-level fMRI analys...
Autoregressive moving average (ARMA) models are important in many fields and applications, although they are most widely applied in time series analysis. Expanding the ARMA models to the case of various complex data is arguably one of the more challe...
This article presents a mathematical model of cyclical economic processes, formulated as the sum of a deterministic polynomial function and a cyclic random process that simultaneously captures trend, stochasticity, cyclicity, and rhythm variability....
Relatively large deformations of the steel arch support in underground coal mines in the Republic of Serbia present one of the main problems for achieving the planned production of coal. Monitoring of the critical sections of the steel arch support i...
This paper proposes a semiparametric realized stochastic volatility model by integrating the parametric stochastic volatility model utilizing realized volatility information and the Bayesian nonparametric framework. The flexible framework offered by...
Predicting the state of a dynamic system influenced by a chaotic immersion environment is an extremely difficult task, in which the direct use of statistical extrapolation computational schemes is infeasible. This paper considers a version of precede...
Reliable short-term traffic flow prediction is crucial for intelligent transportation systems to enable real-time control, mitigate congestion, and improve urban mobility. However, traffic dynamics are inherently uncertain, temporally dependent, and...
Multivariate time series forecasting presents a challenging problem in stochastic modeling, particularly under non-stationary conditions with low signal-to-noise ratios. While recent inverted architectures enhance cross-variable dependency modeling,...
A Markov chain is commonly used in stock market analysis, manpower planning, and in many other areas because of its efficiency in predicting long run behavior. However, the Air Quality Index (AQI) suffers from not using a Markov chain in its forecast...
A large part of the decision-making problems actors of the power system are facing on a daily basis requires scenarios for day-ahead electricity market prices. These scenarios are most likely to be generated based on marginal predictive densities for...
This work studies how the Elo rating system can be applied to score-based sports, where it is gaining popularity, and in particular for predicting the result at any point of a game, extending its statistical basis to stochastic processes. We derive s...
Managing uncertainty remains a fundamental challenge in large-scale engineering projects, where traditional planning techniques frequently produce overly idealistic timelines by failing to account for random events. To address this gap, this research...
The contribution of this article is mainly to develop a new stochastic sequence forecasting model, which is also called the difference iterative forecasting model based on the Generalized Cauchy (GC) process. The GC process is a Long-Range Dependent...
The inherent uncertainty of inflow forecasts hinders the reservoir real-time optimal operation. This paper proposes a risk analysis model for reservoir real-time optimal operation using the scenario tree-based stochastic optimization method. We quant...
During the last decades, thanks to supportive policies of countries and a decrease in installation costs, total installed capacity of wind power has increased rapidly all around the world. The uncertain and variable nature of wind power has been a pr...
In the short-term operation of the power generation of cascade reservoirs, uncertainty factors such as inflow forecast errors could cause various types of risks. The inflow to a downstream reservoir is not only affected by inflow forecast errors from...
We forecast short time series iteratively using a model based on stochastic differential equations. The recorded process is assumed to be consistent with an
Obtaining accurate forecasts for the evolution of epidemic outbreaks from deterministic compartmental models represents a major theoretical challenge. Recently, it has been shown that these models typically exhibit trajectory degeneracy, as different...
In this paper, a fractional Weibull process is utilized in a predictive stochastic differential equation model to allow for skewness and heavy-tailed characteristics. To this aim, a fractional Weibull process with non-Gaussian characteristics and a l...
Long-run forecasts of telecommunication services’ diffusion play an important role in policy, regulation, planning and portfolio decisions. Forecasting diffusion of telecommunication technologies is usually based on S-shaped models, mainly due to the...
This work proposes a stochastic fractional power-law model for currency exchange rate forecasting. The model incorporates nonlocal temporal effects through the Caputo fractional derivative and nonlinear scaling dynamics via a power-law formulation, p...
The paper focuses on the stochastic forecast of the financial sustainability ofbasic pension, based on predictions for the population of China. The population was calculated iteratively by using Leslie matrix. An auto-regressive moving average model...
This article examines the theoretical foundations of economic forecasting based on DSGE models. DSGE models are the main direction of contemporary macroeconomics theory—the inclusion of the stochastic processes and expectations of economic agen...
Accurate short-term electricity load forecasting is crucial for day-ahead scheduling and secure operation of power systems. However, electricity load series exhibit significant non-stationarity, with complex coupling between low-frequency trends and...
Charging load mobility evaluation becomes one of the main concerns for charging services and power system stability due to the stochastic nature of electrical vehicles (EVs) and is critical for the robust scheduling of economic operations at differen...
In a hydropower system, inflow is an uncertain stochastic process that depends on the meteorology of the reservoir’s location. To properly utilize the stored water in reservoirs, it is necessary to have a good forecast or a historical inflow re...
Extrapolative methods are one of the most commonly-adopted forecasting approaches in the literature on projecting future mortality rates. It can be argued that there are two types of mortality models using this approach. The first extracts patterns i...
The deterministic methods generally used to solve DC optimal power flow (OPF) do not fully capture the uncertainty information in wind power, and thus their solutions could be suboptimal. However, the stochastic dynamic AC OPF problem can be used to...
The interferometric synthetic aperture radar (InSAR) technique is widely utilized to measure ground-surface displacement. One of the main limitations of the measurements is the atmospheric phase delay effects. For satellites with shorter wavelengths,...
In recent years, use of the stochastic model has been growing due to the high complexity and dynamics of the atmosphere, especially the rainfall process. Various concepts have been applied to rainfall modeling, ranging from simplistic approaches to m...
Predicting the evolution of financial data, if at all possible, would be very beneficial in revealing the ways in which different aspects of a global environment can impact local economies. We employ an iterative stochastic differential equation that...
The performance evaluation of wind power forecasting under commercially operating circumstances is critical to a wide range of decision-making situations, yet difficult because of its stochastic nature. This paper firstly introduces a novel TRSWA-BP...
The simultaneous stochastic optimization of mining complexes optimizes various components of the related mineral value chain jointly while considering material supply (geological) uncertainty. As a result, the optimization process capitalizes on the...
This article presents a methodology for assessing investment projects representing the sequence of steps necessary for a comprehensive assessment and optimization of an investment project. The assessment process starts with the quantitative forecasti...
Electricity price forecasting (EPF) has become an essential part of decision-making for energy companies to participate in power markets. As the energy mix becomes more uncertain and stochastic, this process has also become important for industrial c...
The increasing penetration of renewable energy introduces greater uncertainty into power system operation, resulting in pronounced stochastic fluctuations in frequency regulation capacity demand and posing increasing challenges to grid security and s...
We consider the problem of time-series forecasting via statistical reconstruction of the coefficients of the Itô representation of the underlying stochastic process
In the actuarial literature, it has become common practice to model future capital returns and mortality rates stochastically in order to capture market risk and forecasting risk. Although interest rates often should and mortality rates always have t...
Parametric continuous-time analysis often entails derivations of continuous-time models from predefined discrete formulations. However, undetermined convergence rates of frequency-dependent parameters can result in ill-defined continuous-time limits,...
Water resource forecasting plays a crucial role in managing hydrological reservoirs, supporting operational decisions ranging from the economy to energy. In recent years, machine learning-based models, including sequential models such as Long Short-T...
With the evolving urbanization process in modern cities, the tertiary industry load and residential load start to take up a major proportion of the total urban power load. These loads are more dependent on stochastic factors such as human behaviors a...
Accurate long-term wind-power forecasting benefits from probabilistic scenario generation that reflects both the temporal dependence of wind conditions and the uncertainty associated with distributional parameters. This study develops a hybrid Statis...
In the competitive electricity wholesale market, decisions regarding hydro generators are generally made under uncertain conditions, such as pool price, hydrological affluence, and other players’ strategies. From this perspective, this work presents...
The behavior of hydrological processes is periodic and stochastic due to the influence of climatic factors. Therefore, it is crucial to develop the models based on their periodicity and stochastic nature for prediction. Furthermore, forecasting the s...
Under dynamic loading conditions, the output voltage of proton exchange membrane fuel cells (PEMFCs) exhibits nonlinear degradation characterized by non-Gaussian fluctuations, abrupt changes, and long-range temporal dependence, which are difficult to...
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