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Forecasting MDPI

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Forecasting MDPI

Dr. Sonia Leva

Forecasting (ISSN 2571-9394) is an international and open access journal of all aspects of forecasting, published quarterly online by MDPI| IF: 2.3 Q2| Citescore: 5.8 Q1 | EiC: Prof.

27/01/2025

Article

"Can Groups Improve Expert Economic and Financial Forecasts?"

Economic and financial forecasts are important for business planning and government policy but are notoriously challenging. We take advantage of recent advances in individual and group judgement, and a data set of economic and financial forecasts compiled over 25 years, consisting of multiple indivi...

27/01/2025

Article

"Bootstrapping State-Space Models: Distribution-Free Estimation in View of Prediction and Forecasting"

Linear models, seasonal autoregressive integrated moving average (SARIMA) models, and state-space models have been widely adopted to model and forecast economic data. While modeling using linear models and SARIMA models is well established in the literature, modeling using state-space models has bee...

24/01/2025

Article

"Automation in Regional Economic Synthetic Index Construction with Uncertainty Measurement"

Subnational jurisdictions, compared to the apparatuses of countries and large institutions, have less resources and human capital available to carry out an updated conjunctural follow-up of the economy (nowcasting) and for generating economic predictions (forecasting). This paper presents the result...

24/01/2025

Article

"An Extended Analysis of Temperature Prediction in Italy: From Sub-Seasonal to Seasonal Timescales"

Earth system predictions, from sub-seasonal to seasonal timescales, remain a challenging task, and the representation of predictability sources on seasonal timescales is a complex work. Nonetheless, advances in technology and science have been making continuous progress in seasonal forecasting. In a...

23/01/2025

📢Highly Cited Paper in
📖 Time-Series Interval with -Output : A Case Study on Durian Exports
✍️by Unyamanee Kummaraka and Patchanok Srisuradetchai

Deep neural networks (DNNs) are prominent in predictive analytics for accurately forecasting target variables. However, inherent uncertainties necessitate constructing prediction intervals for reliability. The existing literature often lacks practical methodologies for creating predictive intervals,...

23/01/2025

📢Highly Cited Paper in
📖 : An Approach Using Cooperative Ensemble Learning with SHapley Additive exPlanations
✍️by Eduardo Luiz Alba, Gilson Adamczuk Oliveira, Matheus Henrique Dal Molin Ribeiro and Érick Oliveira Rodrigues

Electricity expense management presents significant challenges, as this resource is susceptible to various influencing factors. In universities, the demand for this resource is rapidly growing with institutional expansion and has a significant environmental impact. In this study, the machine learnin...

22/01/2025

📢Highly Cited Paper in
📖Impact of and in the Operation of a
✍️by Giampaolo Manzolini, Andrea Fusco, Domenico Gioffrè, Silvana Matrone, Riccardo Ramaschi, Marios Saleptsis, Riccardo Simonetti, Filip Sobic, Michael James Wood, Emanuele Ogliari and Sonia Leva

The electrification of the transport sector together with large renewable energy deployment requires powerful tools to efficiently use energy assets and infrastructure. In this framework, the forecast of electric vehicle demand and solar photovoltaic (PV) generation plays a fundamental role. This pa...

22/01/2025

📢Highly Cited Paper in
📖 in Selected Geographic Regions of Mexico, Associated with
✍️by Victor M. Rodríguez-Moreno and Juan Estrada-Ávalos

In this article, we document the use of hail cannons in Mexico to dispel or suppress heavy rain episodes, a common practice among farmers, without scientific evidence to support its effectiveness. This study uses two rain databases: one compiled from the Global Precipitation Measurement (GPM) missio...

21/01/2025

📢Highly Cited Paper in
📖Can Enhance of Learning Models? A Case of Approach
✍️by C. Tamilselvi, Md Yeasin, Ranjit Kumar Paul and Amrit Kumar Paul

Denoising is an integral part of the data pre-processing pipeline that often works in conjunction with model development for enhancing the quality of data, improving model accuracy, preventing overfitting, and contributing to the overall robustness of predictive models. Algorithms based on a combina...

21/01/2025

📢Highly Cited Paper in
📖Applying and Methods for Enhancing
✍️by Konstantinos P. Fourkiotis and Athanasios Tsadiras

In today’s evolving global world, the pharmaceutical sector faces an emerging challenge, which is the rapid surge of the global population and the consequent growth in drug production demands. Recognizing this, our study explores the urgent need to strengthen pharmaceutical production capacities, ...

20/01/2025

📢Highly Cited Paper in
📖Comprehensive Review of in Applications
✍️by Rejaul Islam, S M Sajjad Hossain Rafin and Osama A. Mohammed

Emerging electric vehicle (EV) technology requires high-voltage energy storage systems, efficient electric motors, electrified power trains, and power converters. If we consider forecasts for EV demand and driving applications, this article comprehensively reviewed power converter topologies, contro...

20/01/2025

📢Highly Cited Paper in
📖On : A Comparison of , , and Ensembles
✍️by Kate Murray, Andrea Rossi, Diego Carraro and Andrea Visentin

Traders and investors are interested in accurately predicting cryptocurrency prices to increase returns and minimize risk. However, due to their uncertainty, volatility, and dynamism, forecasting crypto prices is a challenging time series analysis task. Researchers have proposed predictors based on ...

17/01/2025

📢Highly Cited Paper in
📖A Day-Ahead via and
✍️by Seyed Mahdi Miraftabzadeh, Cristian Giovanni Colombo, Michela Longo and Federica Foiadelli

Climate change and global warming drive many governments and scientists to investigate new renewable and green energy sources. Special attention is on solar panel technology, since solar energy is considered one of the primary renewable sources and solar panels can be installed in domestic neighborh...

17/01/2025

📢Highly Cited Paper in
📖 for in the Fashion and
✍️by Chandadevi Giri and Yan Chen

Compared to other industries, fashion apparel retail faces many challenges in predicting future demand for its products with a high degree of precision. Fashion products’ short life cycle, insufficient historical information, highly uncertain market demand, and periodic seasonal trends necessi...

16/01/2025

📢Highly Cited Paper in
📖Data-Driven Methods for the State of of - : An Overview
✍️by Panagiotis Eleftheriadis, Spyridon Giazitzis, Sonia Leva and Emanuele Ogliari

In recent years, there has been a noticeable shift towards electric mobility and an increasing emphasis on integrating renewable energy sources. Consequently, batteries and their management have been prominent in this context. A vital aspect of the BMS revolves around accurately determining the batt...

16/01/2025

📢Highly Cited Paper in
📖A -MLP Model for on
✍️by Yixuan Li, Charalampos Stasinakis and Wee Meng Yeo

Supply Chain Finance (SCF) has gradually taken on digital characteristics with the rapid development of electronic information technology. Business audit information has become more abundant and complex, which has increased the efficiency and increased the potential risk of commercial banks, with cr...

15/01/2025

📢Highly Cited Paper in
📖 : Their Success and Impact
✍️by Spyros Makridakis, Fotios Petropoulos and Yanfei Kang

ChatGPT, a state-of-the-art large language model (LLM), is revolutionizing the AI field by exhibiting humanlike skills in a range of tasks that include understanding and answering natural language questions, translating languages, writing code, passing professional exams, and even composing poetry, ...

15/01/2025

📢Highly Cited Paper in
📖Day Ahead : A Comprehensive - Methodology and Several Diverse Case Studies
✍️by Michael Wood, Emanuele Ogliari, Alfredo Nespoli, Travis Simpkins and Sonia Leva

Optimal behind-the-meter energy management often requires a day-ahead electric load forecast capable of learning non-linear and non-stationary patterns, due to the spatial disaggregation of loads and concept drift associated with time-varying physics and behavior. There are many promising machine le...

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