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Feature Papers of Forecasting 2021
Sonia Leva (editor)
2022
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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
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Keywords
- aggregated forecasting
- ARCH-GARCH
- artificial neural network
- back-propagation neural network (BPNN)
- bagging
- battery energy storage system
- battery sizing
- building energy management
- combination
- COVID-19
- decision tree
- deep learning
- electrical load
- Environmental science, engineering & technology
- forecast
- Forecasting
- frequency regulation
- harmony search algorithm
- History of engineering & technology
- Holt method
- information
- interpretable machine learning
- K-means clustering
- load curve
- load forecasting
- Long Short-Term Memory (LSTM)
- Loop Current
- LSTM
- Machine learning
- Model Predictive Control
- model-free
- n/a
- Neural Network
- numerical weather prediction
- ocean current forecasting
- ocean measurements
- performance ratio
- photovoltaic power production
- policy
- power outages
- probabilistic graphical models
- Renewable energy sources
- SCADA
- simulation model
- sub-seasonal series
- subsampling bootstrapped
- Technology, engineering, agriculture
- Technology: general issues
- temporal aggregation
- theta
- Thunderstorms
- tobacco endgame
- tobacco tax revenue
- uncertainty
- unevenly spaced time series
- user comfort
- water consumption
Links
DOI: 10.3390/books978-3-0365-5572-0Editions