The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 15, 2021

Filed:

Feb. 15, 2019
Applicant:

Business Objects Software Ltd., Dublin, IE;

Inventors:

Paul Pallath, Naas, IE;

Ying Wu, Maynooth, IE;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/2458 (2019.01); G06F 16/28 (2019.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01);
U.S. Cl.
CPC ...
G06F 16/285 (2019.01); G06F 16/2465 (2019.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01);
Abstract

Techniques are described for performing a time series analysis using a clustering based symbolic representation. Implementations employ a clustering based symbolic representation applied to time series data. In some implementations, the time series data is discretized into subsequences with regular time intervals, and symbols encoding the time intervals may be derived by performing clustering algorithms on the subsequences. In the new representation, a time series is transformed into a sequence of categorical values. The symbolic representation is suitable to perform time series classification and forecast with higher accuracy and greater efficiency compared to previously used techniques. Through use of the symbolic representation, a dimension reduction is applied to transform the time sequences to a feature space with lower dimensions. As output of such transformation, a new representation is obtained based on the original time series. This new reduced-dimension representation improves the efficiency of time series data mining and forecasting.


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