Systems and methods for new time series model probabilistic ARMA
First Claim
1. A system that facilitates predicting values of time observation data in a time series, comprising:
- a component that receives a subset of time observation data comprising at least one selected from the group consisting of discrete time observation data and continuous time observation data; and
an autoregressive, moving average cross-predictions (ARMAxp) model that predicts values of the time observation data, wherein conditional variance of each continuous time tube variable is fixed to a small positive value.
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Abstract
The present invention utilizes a cross-prediction scheme to predict values of discrete and continuous time observation data, wherein conditional variance of each continuous time tube variable is fixed to a small positive value. By allowing cross-predictions in an ARMA based model, values of continuous and discrete observations in a time series are accurately predicted. The present invention accomplishes this by extending an ARMA model such that a first time series “tube” is utilized to facilitate or “cross-predict” values in a second time series tube to form an “ARMAxp” model. In general, in the ARMAxp model, the distribution of each continuous variable is a decision graph having splits only on discrete variables and having linear regressions with continuous regressors at all leaves, and the distribution of each discrete variable is a decision graph having splits only on discrete variables and having additional distributions at all leaves.
57 Citations
31 Claims
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1. A system that facilitates predicting values of time observation data in a time series, comprising:
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a component that receives a subset of time observation data comprising at least one selected from the group consisting of discrete time observation data and continuous time observation data; and
an autoregressive, moving average cross-predictions (ARMAxp) model that predicts values of the time observation data, wherein conditional variance of each continuous time tube variable is fixed to a small positive value. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 28, 30)
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12. A method of predicting values of time observation data in a time series, comprising:
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providing a subset of time observation data comprising at least one selected from the group consisting of discrete time observation data and continuous time observation data; and
predicting values of the time observation data utilizing an autoregressive, moving average cross-predictions (ARMAxp) model;
wherein conditional variance of each continuous variable is fixed to a small positive value. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20, 21, 29, 31)
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22. A system for predicting values of time observation data in a time series, comprising:
means for predicting values of time observation data utilizing an autoregressive, moving average cross-predictions (ARMAxp) model;
wherein conditional variance of each continuous time tube variable is fixed to a small positive value.- View Dependent Claims (23, 24, 25, 26)
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27. A data packet transmitted between two or more computer components that facilitates predicting values of time observation data in a time series, the data packet comprising, at least in part, time observation prediction data based, at least in part, on an autoregressive, moving average cross-predictions (ARMAxp) model;
- wherein conditional variance of each continuous time tube variable is fixed to a small positive value.
Specification