Feature Papers of Forecasting 2021
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 t...
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Format: | Electronic Book Chapter |
Language: | English |
Published: |
Basel
MDPI - Multidisciplinary Digital Publishing Institute
2022
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Subjects: | |
Online Access: | DOAB: download the publication DOAB: description of the publication |
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260 | |a Basel |b MDPI - Multidisciplinary Digital Publishing Institute |c 2022 | ||
300 | |a 1 electronic resource (196 p.) | ||
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506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
520 | |a 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 | ||
540 | |a Creative Commons |f https://creativecommons.org/licenses/by/4.0/ |2 cc |4 https://creativecommons.org/licenses/by/4.0/ | ||
546 | |a English | ||
650 | 7 | |a Technology: general issues |2 bicssc | |
650 | 7 | |a History of engineering & technology |2 bicssc | |
650 | 7 | |a Environmental science, engineering & technology |2 bicssc | |
653 | |a renewable energy sources | ||
653 | |a load forecasting | ||
653 | |a frequency regulation | ||
653 | |a artificial neural network | ||
653 | |a model predictive control | ||
653 | |a building energy management | ||
653 | |a forecast | ||
653 | |a neural network | ||
653 | |a SCADA | ||
653 | |a user comfort | ||
653 | |a tobacco endgame | ||
653 | |a policy | ||
653 | |a simulation model | ||
653 | |a tobacco tax revenue | ||
653 | |a information | ||
653 | |a combination | ||
653 | |a uncertainty | ||
653 | |a theta | ||
653 | |a temporal aggregation | ||
653 | |a bagging | ||
653 | |a sub-seasonal series | ||
653 | |a power outages | ||
653 | |a machine learning | ||
653 | |a thunderstorms | ||
653 | |a numerical weather prediction | ||
653 | |a battery energy storage system | ||
653 | |a battery sizing | ||
653 | |a photovoltaic power production | ||
653 | |a performance ratio | ||
653 | |a electrical load | ||
653 | |a decision tree | ||
653 | |a k-means clustering | ||
653 | |a load curve | ||
653 | |a unevenly spaced time series | ||
653 | |a long short-term memory (LSTM) | ||
653 | |a back-propagation neural network (BPNN) | ||
653 | |a water consumption | ||
653 | |a Holt method | ||
653 | |a subsampling bootstrapped | ||
653 | |a harmony search algorithm | ||
653 | |a forecasting | ||
653 | |a ARCH-GARCH | ||
653 | |a model-free | ||
653 | |a aggregated forecasting | ||
653 | |a deep learning | ||
653 | |a Loop Current | ||
653 | |a ocean current forecasting | ||
653 | |a LSTM | ||
653 | |a ocean measurements | ||
653 | |a COVID-19 | ||
653 | |a probabilistic graphical models | ||
653 | |a interpretable machine learning | ||
653 | |a n/a | ||
856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/6266 |7 0 |z DOAB: download the publication |
856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/93837 |7 0 |z DOAB: description of the publication |