Pattern discovery in multi-dimensional time series using multi-resolution matching
First Claim
Patent Images
1. A computerized method for discovering patterns in unknown multi-dimensional data, comprising:
- generating a time series of the multi-dimensional data;
constructing a point cross-distance matrix by self-correlating the time series;
locating all minimum cost paths in the point cross-distance matrix at a plurality of time resolutions; and
relating the minimum cost paths to sub-sequences in the multi-dimensional data to discover high-level patterns in the unknown multi-dimensional data.
1 Assignment
0 Petitions
Accused Products
Abstract
A method discovers patterns in unknown multi-dimensional data. A time-series of the multi-dimensional data is generated and a point cross-distance matrix is constructed by self-correlating the time-series. All minimum cost paths in the point cross-distance matrix are located at multiple time resolutions. The minimum cost paths are then related to temporal sub-sequences in the multi-dimensional data to discover high-level patterns in the unknown multi-dimensional data.
35 Citations
23 Claims
-
1. A computerized method for discovering patterns in unknown multi-dimensional data, comprising:
-
generating a time series of the multi-dimensional data;
constructing a point cross-distance matrix by self-correlating the time series;
locating all minimum cost paths in the point cross-distance matrix at a plurality of time resolutions; and
relating the minimum cost paths to sub-sequences in the multi-dimensional data to discover high-level patterns in the unknown multi-dimensional data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23)
-
Specification