초록 ▼

Arrangements and methods for performing structural clustering between different time series. Time series data relating to a plurality of time series is accepted, structural features relating to the time series data are ascertained, and at least one distance between different time series via employin

Arrangements and methods for performing structural clustering between different time series. Time series data relating to a plurality of time series is accepted, structural features relating to the time series data are ascertained, and at least one distance between different time series via employing the structural features is determined. The different time series may be partitioned into clusters based on the at least one distance, and/or the k closest matches to a given time series query based on the at least one distance may be returned.

대표청구항 ▼

What is claimed is: 1. A method of performing structural clustering between different time series, said method comprising the steps of: accepting time series data relating to a plurality of time series; ascertaining structural features relating to the time series data; determining at least one dist

What is claimed is: 1. A method of performing structural clustering between different time series, said method comprising the steps of: accepting time series data relating to a plurality of time series; ascertaining structural features relating to the time series data; determining at least one distance between different time series via employing the structural features; and partitioning the different time series into time-invariant clusters containing at least one of the time series based on the at least one distance; wherein the clusters are stored in a computer memory; wherein said ascertaining step comprises: computing all structural features; and automatically selecting a number of most relevant features; and wherein said step of automatically selecting a number of most relevant features comprises: selecting a threshold; and retaining features having value larger than the threshold; and said step of selecting a threshold comprises selecting a threshold which serves to discard features having values attributable to statistical variations via: computing a resampling estimate of the distribution of feature values attributable to statistical variations; selecting a value of probability of type 1 error; and selecting as a threshold a value that guarantees the selected value of probability of type 1 error for a distribution equal to the resampling estimate of the distribution. 2. The method according to claim 1, further comprising the step of determining common periodicities corresponding to each of the clusters. 3. The method according to claim 1, further comprising the step of predetermining a number of structural features to compute. 4. The method according to claim 1, wherein: said ascertaining step comprises ascertaining frequency content relating to the time series data; and said ascertaining step further comprises implementing a Discrete Fourier Transform. 5. The method according to claim 1, wherein: said ascertaining step comprises determining an orthogonal transformation relating to the time series data; the orthogonal transformation of the data comprising a Discrete Wavelet Transform. 6. The method according to claim 1, further comprising the steps of: selecting at least one of said structural features; and said determining is performed via employing the at least one structural feature selected. 7. The method according to claim 6, wherein said selecting step is performed by a user. 8. The method according to claim 6, wherein: said selecting step is performed automatically; and said selecting step comprises: identifying candidate structural features; and verifying the candidate structural features. 9. The method according to claim 8, wherein: said identifying step comprises: computing a periodogram of the different time series; and identifying peaks of the periodogram; and said verifying step comprises: computing an autocorrelation; and selecting identified peaks of the periodogram that lie on hills of the autocorrelation. 10. The method according to claim 1, wherein said ascertaining step comprises: computing all structural features; and automatically selecting a number of most relevant features. 11. The method according to claim 10, wherein said step of automatically selecting a number of most relevant features comprises: selecting a parameter k corresponding to a number of structural features to keep; and retaining the k features that contain the highest amount of periodic content.