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» Forecasting high-dimensional data
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ESANN
2000
14 years 11 months ago
Nonlinear prediction of spatio-temporal time series
Abstract. A prediction scheme for spatio-temporal time series is presented that is based on reconstructed local states. As a numerical example the ev olution of a Kuramoto-Sivashin...
Ulrich Parlitz, Christian Merkwirth
SIGPRO
2002
72views more  SIGPRO 2002»
14 years 9 months ago
Modeling electricity loads in California: ARMA models with hyperbolic noise
In this paper we address the issue of modeling and forecasting electricity loads. We apply a two-step procedure to a series of system-wide loads from the California power market. ...
J. Nowicka-Zagrajek, R. Weron
NIPS
2000
14 years 11 months ago
A Support Vector Method for Clustering
We present a novel method for clustering using the support vector machine approach. Data points are mapped to a high dimensional feature space, where support vectors are used to d...
Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladi...
SCHOLARPEDIA
2008
89views more  SCHOLARPEDIA 2008»
14 years 8 months ago
Support vector clustering
We present a novel method for clustering using the support vector machine approach. Data points are mapped to a high dimensional feature space, where support vectors are used to d...
Asa Ben-Hur
KDD
1999
ACM
220views Data Mining» more  KDD 1999»
15 years 2 months ago
Efficient Mining of Emerging Patterns: Discovering Trends and Differences
We introduce a new kind of patterns, called emerging patterns (EPs), for knowledge discovery from databases. EPs are defined as itemsets whose supports increase significantly from...
Guozhu Dong, Jinyan Li