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Feature Papers of Forecasting
Sonia Leva (editor)
2021
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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.
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Keywords
- (REG) ARIMA
- baseline prediction
- Bates–Granger weights
- behind-the-meter PV
- bias correction
- bus service
- bus travel time
- business to business sales prediction
- cost estimating
- costumer relation management
- COVID-19
- deep learning
- Direct Normal Irradiance (DNI)
- DNI attenuation Index (DAI)
- ETS
- Evaluation
- forecast
- Forecasting
- Google Trends indices
- Himalayan region
- Hodrick–Prescott trend
- IFS/ECMWF
- intermittent rivers
- learning curve
- logistic map
- Machine learning
- meteorological radar data
- microsoft azure machine-learning service
- n/a
- nowcast
- optical flow
- Persistence
- predictive modeling
- production cost
- PV output power estimation
- PV-load decoupling
- Reference, information & interdisciplinary subjects
- Research & information: general
- SARS-CoV-2
- semi-empirical model
- snow-fed rivers
- streamflow forecast verification
- sustainable urban mobility plan
- thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general
- time series
- transit systems
- travel time forecasting
- uniform weights
Links
DOI: 10.3390/books978-3-0365-1031-6Editions