Gradient learning for probabilistic ARMA time-series models
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United States of America Patent
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Sep 2, 2008
Grant Date -
Jun 15, 2006
app pub date -
Dec 14, 2004
filing date -
Dec 14, 2004
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Abstract
The subject invention leverages the conditional Gaussian (CG) nature of a continuous variable stochastic ARMA.sup.xp time series model to efficiently determine its parametric gradients. The determined gradients permit an easy means to construct a parametric structure for the time series model. This provides a gradient-based alternative to the expectation maximization (EM) process for learning parameters of the stochastic ARMA.sup.xp time series model. Thus, gradients for parameters can be computed and utilized with a gradient-based learning method for estimating the parameters. This allows values of continuous observations in a time series to be predicted utilizing the stochastic ARMA.sup.xp time series model, providing efficient and accurate predictions.
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