Online Prediction of Time Series Data With Kernels

By Cédric Richard, José Carlos Moreira Bermudez, Paul Honeine. IEEE Transactions on Signal Processing, 2009

444 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mechanics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/5cf1abeb7fcfa887130429d4274212591c28be74

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jie Chen
2. Ali H. Sayed
3. Cédric Richard
4. Paul Honeine
5. Kun Yuan
6. Jianshu Chen
7. Bicheng Ying
8. Angelia Nedić

In Computational Mechanics

1. Jie Chen
2. Cédric Richard
3. Ali H. Sayed
4. Paul Honeine
5. Jianshu Chen
6. Mohammad Rostami
7. Angelia Nedić
8. Alexander Olshevsky

In Signal Processing

1. Jie Chen
2. Ali H. Sayed
3. Cédric Richard
4. Paul Honeine
5. Jianshu Chen

Citations in the corpus

• Multitask Diffusion Adaptation Over Networks (2013, methodology): “Distributed estimation based on adaptive kernel regression [34], [35] is also studied in [36]–[39], with information passed from node to node in an incremental manner.”
• Online Kernel Principal Component Analysis: A Reduced-Order Model (2012, background): “In order to take advantage of the literature of dictionaries in sparse approximation [23], [45], [46], we consider the case of positive kernels with unit-norm,(3) i.”
• Short-term load forecasting based on multivariate time series prediction and weighted neural network with random weights and kernels (2018, methodology): “In order to solve the problem of time series prediction, support vector machines [19], neural networks [20–24], and other machine learning methods [25– 27] have been introduced to the chaotic time series prediction field.”