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Feature Papers of Forecasting 2021 / Sonia Leva.
- Format:
- Book
- Author/Creator:
- Leva, Sonia, author.
- Language:
- English
- Subjects (All):
- Computer science.
- Computer engineering.
- Physical Description:
- 1 online resource (196 pages)
- Place of Publication:
- Basel, Switzerland : MDPI - Multidisciplinary Digital Publishing Institute, 2022.
- Summary:
- 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.
- Contents:
- 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.
- Notes:
- Description based on publisher supplied metadata and other sources.
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