Modeling and Stochastic Learning for Forecasting in High Dimensions /
The chapters in this volume stress the need for advances in theoretical understanding to go hand-in-hand with the widespread practical application of forecasting in industry. Forecasting and time series prediction have enjoyed considerable attention over the last few decades, fostered by impressive...
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| Other Authors: | , , |
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cham :
Springer International Publishing : Imprint: Springer,
2015.
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| Series: | Lecture notes in statistics (Springer-Verlag) ;
217. |
| Subjects: | |
| Online Access: | Connect to this title online |
Table of Contents:
- 1 Short Term Load Forecasting in the Industry for Establishing Consumption Baselines: A French Case
- 2 Confidence intervals and tests for high-dimensional models: a compact review
- 3 Modelling and forecasting daily electricity load via curve linear regression
- 4 Constructing Graphical Models via the Focused Information Criterion
- 5 Nonparametric short term Forecasting electricity consumption with IBR
- 6 Forecasting the electricity consumption by aggregating experts
- 7 Flexible and dynamic modeling of dependencies via copulas
- 8 Operational and online residential baseline estimation
- 9 Forecasting intra day load curves using sparse functional regression
- 10 Modelling and Prediction of Time Series Arising on a Graph
- 11 GAM model based large scale electrical load simulation for smart grids
- 12 Spot volatility estimation for high-frequency data: adaptive estimation in practice
- 13 Time series prediction via aggregation: an oracle bound including numerical cost
- 14 Space-time trajectories of wind power generation: Parametrized precision matrices under a Gaussian copula approach
- 15 Game-theoretically Optimal Reconciliation of Contemporaneous Hierarchical Time Series Forecasts
- 16 The BAGIDIS distance: about a fractal topology, with applications to functional classification and prediction.