Feature Papers of Forecasting 2021 / / Sonia Leva

• Record Nr.: UNINA9910674386203321
• Autore: Leva Sonia
• Pubbl/distr/stampa: Basel, Switzerland : , : MDPI - Multidisciplinary Digital Publishing Institute, , 2022
• Descrizione fisica: 1 online resource (196 pages)
• Disciplina: 004
• Soggetti: Computer science; Computer engineering
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: About the Editor -- Editorial for Special Issue: "Feature Papers of Forecasting 2021" -- SIMLR: Machine Learning inside the SIR Model for COVID-19 Forecasting -- A Deep Learning Model for Forecasting Velocity Structures of the Loop Current System in the Gulf of Mexico -- Model-Free Time-Aggregated Predictions for Econometric Datasets -- Bootstrapped Holt Method with Autoregressive Coefficients Based on Harmony Search Algorithm -- A Real-Time Data Analysis Platform for Short-Term Water Consumption Forecasting with Machine Learning -- Battery Sizing for Different Loads and RES Production Scenarios through Unsupervised Clustering Methods -- Influence of the Characteristics of Weather Information in a Thunderstorm-Related Power Outage Prediction System -- Tobacco Endgame Simulation Modelling: Assessing the Impact of Policy Changes on Smoking revalence in 2035 -- Load Forecasting in an Office Building with Different Data Structure and Learning Parameters -- A Model Predictive Control for the Dynamical Forecast of Operating Reserves in Frequency egulation Services -- The Wisdom of the Data: Getting the Most Out of Univariate Time Series Forecasting.
• Sommario/riassunto: This book focuses on fundamental and applied research on forecasting methods and analyses on how forecasting can affect a great number of fields, spanning from Computer Science, Engineering, and Economics and Business to natural sciences. Forecasting applications are increasingly important because they allow for improving decision-; making processes by providing useful insights about the future. Scientific research is giving unprecedented attention to forecasting applications, with a continuously growing number of articles about novel forecast approaches being published.