Reprint
Feature Papers of Forecasting
Edited by
August 2021
186 pages
- ISBN978-3-0365-1030-9 (Hardback)
- ISBN978-3-0365-1031-6 (PDF)
This book is a reprint of the Special Issue Feature Papers of Forecasting that was published in
Business & Economics
Computer Science & Mathematics
Engineering
Environmental & Earth Sciences
Summary
Nowadays, forecast applications are receiving unprecedent attention thanks to their capability to improve the decision-making processes by providing useful indications. A large number of forecast approaches related to different forecast horizons and to the specific problem that have to be predicted have been proposed in recent scientific literature, from physical models to data-driven statistic and machine learning approaches. In this Special Issue, the most recent and high-quality researches about forecast are collected. A total of nine papers have been selected to represent a wide range of applications, from weather and environmental predictions to economic and management forecasts. Finally, some applications related to the forecasting of the different phases of COVID in Spain and the photovoltaic power production have been presented.
Format
- Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
Direct Normal Irradiance (DNI); IFS/ECMWF; forecast; evaluation; DNI attenuation Index (DAI); bias correction; nowcast; meteorological radar data; optical flow; deep learning; Bates–Granger weights; uniform weights; (REG) ARIMA; ETS; Hodrick–Prescott trend; Google Trends indices; Himalayan region; streamflow forecast verification; persistence; snow-fed rivers; intermittent rivers; costumer relation management; business to business sales prediction; machine learning; predictive modeling; microsoft azure machine-learning service; travel time forecasting; time series; bus service; transit systems; sustainable urban mobility plan; bus travel time; learning curve; forecasting; production cost; cost estimating; semi-empirical model; logistic map; COVID-19; SARS-CoV-2; PV output power estimation; PV-load decoupling; behind-the-meter PV; baseline prediction; n/a