Support vector machines experts for time series forecasting

By Lijuan Cao. Neurocomputing, 2003

420 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/38ed2365b3feecf86d3442460e5b83438e14936f

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. Ingo Fischer
2. Miguel C. Soriano
3. Claudio R. Mirasso
4. Erdal Kayacan
5. Daniel Brunner
6. Trung-Quôc Le
7. Okyay Kaynak
8. Stephan K. Chalup

In Electrical and Electronic Engineering

1. Ingo Fischer
2. Erdal Kayacan
3. Miguel C. Soriano
4. Daniel Brunner
5. Trung-Quôc Le
6. Okyay Kaynak
7. Stephan K. Chalup
8. Paul D. Gader

In Management Science and Operations Research

1. Erdal Kayacan
2. Miguel C. Soriano
3. Daniel Brunner
4. Trung-Quôc Le
5. Okyay Kaynak
6. Stephan K. Chalup
7. Paul D. Gader
8. Satish T. S. Bukkapatnam

Citations in the corpus

• Grey system theory-based models in time series prediction (2010, methodology): “Other intelligent approaches seen in the literature for the analysis of time series include Linear regression, Kalman filtering (Ma & Teng, 2004), fuzzy systems (Kandel, 1991), hidden markov models (Rabiner, 1989) and the support vector machines (Cao, 2003).”
• Time series forecasting for nonlinear and non-stationary processes: a review and comparative study (2015, methodology): “Cao’s SVM expert forecasting model (Cao, 2003) uses a two-stage architecture: an SOM to partition the input space into several disjointed regions and multiple SVMs (SVM experts) to provide forecasts in each of the partitioned regions.”
• Parallel photonic information processing at gigabyte per second data rates using transient states (2013, methodology): “Time-series prediction using machine learning techniques and realized in software, achieved errors below 1% (refs 20,21).”
• Twenty Years of Mixture of Experts (2012, methodology): “3) Models for the experts. a) Gaussian [2], [59], multinomial [23], [60], generalized Bernoulli [51], GP [55], [61], SVM [12], [14].”
• Kernel Methods in Finance (2008, methodology): “…sequence classification (Sonnenburg et al., 2005), other applications in computational biology (Schölkopf et al., 2004), time series prediction (Cao, 2003), text categorization (Fu et al., 2004), handwritten digit recognition (Vapnik, 2000), pedestrian detection (Kang et al., 2002; Chen et…”
• A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy (2007, neutral): “…face identification), text categorization, bioinformatics (e.g., gene expression), function approximation and regression, time series forecasting (Cao, 2003; Kim, 2003; Mukherjee, Osuna, & Girosi, 1997; Müller, Smola, Rätsch, & Schölkopf, 1999; Tay & Cao, 2001, 2002), chaotic system (Mattera…”