Estimation of future mortality rates still plays a central role among life insurers in pricing their products and managing longevity risk. In the literature on mortality modeling, a wide number of stochastic models have been proposed, most of them fo...
Weather forecasting, a vital task for agriculture, transportation, energy, etc., has evolved significantly over the years. Comprehensive surveys play a crucial role in synthesizing knowledge, identifying trends, and addressing emerging challenges in...
Bitcoin is one of the cryptocurrencies that has gained popularity in recent years. Previous studies have shown that closing price alone is not enough to forecast its future level, and other price-related features are necessary to improve forecast acc...
Hospital readmissions are regarded as a compounding economic factor for healthcare systems. In fact, the readmission rate is used in many countries as an indicator of the quality of services provided by a health institution. The ability to forecast p...
The ability to accurately forecast the spot price of natural gas benefits stakeholders and is a valuable tool for all market participants in the competitive gas market. In this paper, we attempt to forecast the natural gas spot price 1, 3, 5, and 10...
In this paper, we study the usage of machine-learning models for sales predictive analytics. The main goal of this paper is to consider main approaches and case studies of using machine learning for sales forecasting. The effect of machine-learning g...
Electricity peak load forecasting plays an important role in electricity generation capacity planning to ensure reliable power supplies. To achieve high forecast accuracy, multiple machine learning models have been implemented to forecast the monthly...
The problem of forecasting time series is very widely debated. In recent years, machine learning algorithms have been very prolific in this area. This paper describes a systematic approach to building a machine learning predictive model for solving o...
Macroeconomic forecasting has become increasingly challenging in environments characterized by structural breaks, nonlinear dynamics, and elevated economic uncertainty. Traditional econometric forecasting models frequently experience substantial pred...
Machine learning forecasting methods are compared to more traditional parametric statistical models. This comparison is carried out regarding a number of different situations and settings. A survey of the most used parametric models is given. Machine...
The accuracy of weather forecasting hinges crucially on the availability of comprehensive historical weather data. In remote regions face the challenge of sparse data collection, impacting the accuracy of meteorological predictions. This study delves...
The electricity market is a complex, evolutionary, and dynamic environment. Forecasting electricity prices is an important issue for all electricity market participants. In this study, we shed light on how to improve electricity price forecasting acc...
Electric energy demand forecasting is very important for electric utilities to procure and supply electric energy for consumers sufficiently, safely, reliably, and continuously. Consequently, the processing time and accuracy of the forecast system ar...
The regional demand for electric power is influenced by a variety of factors, such as fluctuations in business cycles, dynamic linkages among regional development, and climate change. The valid quantification of the impacts of these factors on the de...
Advancements in renewable energy technology have significantly reduced the consumer dependence on conventional energy sources for power generation. Solar energy has proven to be a sustainable source of power generation compared to other renewable ene...
Accurate forecasting of insurance claims is of the utmost importance for insurance activity as the evolution of claims determines cash outflows and the pricing, and thus the profitability, of the underlying insurance coverage. These are used as input...
Water scarcity is a global problem. Deficit irrigation (DI) reduces evapotranspiration, improving water efficiency in agriculture. Reference evapotranspiration
Public health is now in danger because of the current monkeypox outbreak, which has spread rapidly to more than 40 countries outside of Africa. The growing monkeypox epidemic has been classified as a “public health emergency of international co...
Accurate wind power forecasting is essential for the stable and economic operation of power systems with high renewable penetration. Although machine learning models have been widely adopted for this task, the assumption that greater model complexity...
Accurate predictions of photovoltaic generation are essential for effectively managing power system resources, particularly in the face of high variability in solar radiation. This is especially crucial in microgrids and grids, where the proper opera...
An accurate short-term load forecasting (STLF) is one of the most critical inputs for power plant units’ planning commitment. STLF reduces the overall planning uncertainty added by the intermittent production of renewable sources; thus, it help...
In light of challenges like climate change, pollution, and depletion of fossil fuel reserves, governments and businesses prioritize renewable energy sources such as solar, wind, and hydroelectric power. Renewable energy forecasting models play a cruc...
Efforts to understand the influence of historical climate change, at global and regional levels, have been increasing over the past decade. In particular, the estimates of air temperatures have been considered as a key factor in climate impact studie...
Feature engineering is an essential step in the pipelines used for many machine learning tasks, including time-series forecasting. Although existing AutoML approaches partly automate feature engineering, they do not support specialised approaches for...
The ongoing upsurge of deep learning and artificial intelligence methodologies manifest incredible accomplishment in a broad scope of assessing issues in different industries, including the energy sector. In this article, we have presented a hybrid e...
Patenting is essential for protecting intellectual property, fostering technological innovation, and maintaining competitive advantages in the global market. In Mexico, strategic planning in science, technology, and innovation requires reliable forec...
The increasing trend in energy demand is higher than the one from renewable generation, in the coming years. One of the greatest sources of consumption are buildings. The energy management of a building by means of the production of photovoltaic ener...
Earthquakes are one of the most overwhelming types of natural hazards. As a result, successfully handling the situation they create is crucial. Due to earthquakes, many lives can be lost, alongside devastating impacts to the economy. The ability to f...
Forecasting the electricity load provides its future trends, consumption patterns and its usage. There is no proper strategy to monitor the energy consumption and generation; and high variation among them. Many strategies are used to overcome this pr...
Storage rate forecasting for the agricultural reservoir is helpful for preemptive responses to disasters such as agricultural drought and planning so as to maintain a stable agricultural water supply. In this study, SVM, RF, and ANN machine learning...
This article presents a review of current advances and prospects in the field of forecasting renewable energy generation using machine learning (ML) and deep learning (DL) techniques. With the increasing penetration of renewable energy sources (RES)...
The integration of photovoltaic (PV) systems into the global energy landscape has been boosted in recent years, driven by environmental concerns and research into renewable energy sources. The accurate prediction of temperature and solar irradiance i...
The mechanisms of data analytics and machine learning can allow for a profound conceptualization of viruses (such as pathogen transmission rate and behavior). Consequently, such models have been widely employed to provide rapid and accurate viral spr...
The unpredictable nature of renewable energy sources (RES) has become a major cause of imbalance between electricity generation and demand, which the Transmission System Operator (TSO) must handle in real-time operation. The RES stochasticity and the...
To achieve high-precision forecasting of different grades of albacore fishing grounds in the South Pacific Ocean, we used albacore fishing data and marine environmental factors data from 2009 to 2019 as data sources. An ensemble learning model (ELM)...
The wind is a crucial factor in various domains such as weather forecasting, the wind power industry, agriculture, structural health monitoring, and so on. The variability and unpredictable nature of the wind is a challenge faced by most wind-energy-...
In this study, an improved version of the Extreme Learning Machine, namely the Improved Weighted Regularization ELM (IWRELM), is proposed for hourly precipitation forecasting that is multi-steps ahead. After finding the optimal values of the proposed...
Accurate solar radiation forecasting is essential to operate power systems safely under high shares of photovoltaic generation. This paper compares the performance of several machine learning algorithms for solar radiation forecasting using endogenou...
The importance of forecasting crop yields in agriculture cannot be overstated. The effects of yield forecasting are observed in all the aspects of the supply chain from staffing to supplier demand, food waste, and other business decisions. However, t...
This paper proposes a new combined semiparametric estimator of the conditional variance that takes the product of a parametric estimator and a nonparametric estimator based on machine learning. A popular kernel-based machine learning algorithm, known...
The importance of an interference-less machine learning scheme in time series prediction is crucial, as an oversight can have a negative cumulative effect, especially when predicting many steps ahead of the currently available data. The on-going rese...
Accurate sales forecasting is essential for optimizing resource allocation, managing inventory, and maximizing profit in competitive markets. Machine learning models are being increasingly used to develop reliable sales-forecasting systems due to the...
Islamic finance has rapidly expanded into a major component of global financial systems, positioning Sukuk as a core instrument for Sharia-compliant investment and capital raising. Despite growing academic attention to Sukuk pricing and valuation, to...
This research provides a thorough examination of the industrial sector’s forecasting of renewable energy consumption, utilizing sophisticated machine learning techniques to enhance the accuracy and reliability of the predictions. LASSO regressi...
The purpose of this paper is to forecast the sovereign credit risk for Egypt, Morocco, and Saudi Arabia during political crises. Our approach uses machine learning models (Linear Regression, Ridge Regression, Lasso Regression, XGBoost, and Kernel Rid...
This study addresses the critical need for accurate tourism demand (TD) forecasting in Bulgaria using economic indicators, developing robust predictive models to navigate post-pandemic market volatility. The COVID-19 pandemic exposed tourism’s...
A main challenge for integrating the intermittent photovoltaic (PV) power generation remains the accuracy of day-ahead forecasts and the establishment of robust performing methods. The purpose of this work is to address these technological challenges...
This study applied various machine learning techniques, including shrinkage methods, XGBoost, CSR, and random forest, to forecast ultrafine particulate matter (PM2.5) concentrations in Seoul, South Korea. The analysis incorporated key variables known...
The lunar calendar is often overlooked in time-series data modeling despite its importance in understanding seasonal patterns, as well as economics, natural phenomena, and consumer behavior. This study aimed to investigate the effectiveness of the lu...
Accurate Day-Ahead Energy Price (DAEP) forecasting is essential for optimizing energy market operations. This study introduces a machine learning framework to predict the DAEP with a 24 h lead time, leveraging historical data and forecasts available...
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