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Modeling and Stochastic Learning for Forecasting in High Dimensions

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Cover of 'Modeling and Stochastic Learning for Forecasting in High Dimensions'

Table of Contents

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    Book Overview
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    Chapter 1 Short Term Load Forecasting in the Industry for Establishing Consumption Baselines: A French Case
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    Chapter 2 Confidence Intervals and Tests for High-Dimensional Models: A Compact Review
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    Chapter 3 Modelling and Forecasting Daily Electricity Load via Curve Linear Regression
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    Chapter 4 Constructing Graphical Models via the Focused Information Criterion
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    Chapter 5 Fully Nonparametric Short Term Forecasting Electricity Consumption
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    Chapter 6 Forecasting Electricity Consumption by Aggregating Experts; How to Design a Good Set of Experts
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    Chapter 7 Flexible and Dynamic Modeling of Dependencies via Copulas
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    Chapter 8 Online Residential Demand Reduction Estimation Through Control Group Selection
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    Chapter 9 Forecasting Intra Day Load Curves Using Sparse Functional Regression
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    Chapter 10 Modelling and Prediction of Time Series Arising on a Graph
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    Chapter 11 Massive-Scale Simulation of Electrical Load in Smart Grids Using Generalized Additive Models
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    Chapter 12 Spot Volatility Estimation for High-Frequency Data: Adaptive Estimation in Practice
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    Chapter 13 Time Series Prediction via Aggregation: An Oracle Bound Including Numerical Cost
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    Chapter 14 Space-Time Trajectories of Wind Power Generation: Parametrized Precision Matrices Under a Gaussian Copula Approach
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    Chapter 15 Game-Theoretically Optimal Reconciliation of Contemporaneous Hierarchical Time Series Forecasts
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    Chapter 16 The BAGIDIS Distance: About a Fractal Topology, with Applications to Functional Classification and Prediction
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Modeling and Stochastic Learning for Forecasting in High Dimensions
Published by
Springer International Publishing, January 2015
DOI 10.1007/978-3-319-18732-7
978-3-31-918731-0, 978-3-31-918732-7

Anestis Antoniadis, Jean-Michel Poggi, Xavier Brossat

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Colombia 1 2%
China 1 2%
Unknown 44 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 20%
Student > Master 8 17%
Researcher 5 11%
Professor > Associate Professor 4 9%
Student > Bachelor 3 7%
Other 8 17%
Unknown 9 20%
Readers by discipline Count As %
Computer Science 8 17%
Mathematics 8 17%
Economics, Econometrics and Finance 6 13%
Engineering 5 11%
Materials Science 2 4%
Other 7 15%
Unknown 10 22%