COMPUTER-IMPLEMENTED METHOD FOR RECOGNIZING AN INPUT PATTERN IN AT LEAST ONE TIME SERIES OF A PLURALITY OF TIME SERIES

Abstract

A method for recognizing an input pattern in at least one time series is provided including a. providing the time series; b. generating associated time series sections of a specific length on the basis of the time series by a combination of statistical approaches or a machine learning model; c. indexing each time series section; d. assigning each time series section to an applicable key value index; e. recognizing the input pattern in at least one time series of the plurality of time series by identifying at least one time series section that matches or is similar to the input pattern by a similarity search approach on the basis of the plurality of indexed time series sections; and f. providing the at least one identified time series section as an output pattern that matches or is similar to the input pattern if a match or similarity is detected.

Claims

1. A computer-implemented method for recognizing an input pattern in at least one time series of a plurality of time series; wherein the input pattern is a time series section of a specific length, the method comprising: a. providing the plurality of time series, wherein: each time series of the plurality of time series comprises a chronologically ordered sequence of input data; b. generating a plurality of associated time series sections of a specific length on a basis of the plurality of time series by a combination of statistical approaches or a machine learning model, wherein: the machine learning model was trained on at least some of the plurality of time series using the combination of statistical approaches; c. indexing each time series section of the plurality of time series sections; d. assigning each time series section to an applicable key value index, wherein: the respective key value index comprises a numerical vector that denotes the respective time series section as a key and the at least one position or the at least one place in the respective time series as a value; e. recognizing the input pattern in at least one time series of the plurality of time series by identifying at least one time series section that matches or is similar to the input pattern by a similarity search approach on a basis of the plurality of indexed time series sections; and f. providing the at least one identified time series section as an output pattern that matches or is similar to the input pattern if a match or similarity is detected.

2. The computer-implemented method as claimed in claim 1, wherein the input pattern is input by a user via an input interface, by a manual input or a voice input.

3. The computer-implemented method as claimed in claim 1, wherein the plurality of time series and/or the plurality of associated time series sections are stored in a database or cloud.

4. The computer-implemented method as claimed in claim 1, wherein the input data are acquired by way of a data acquisition unit, the data acquisition unit being a sensor unit, a camera unit, or an image recognition unit.

5. The computer-implemented method as claimed in claim 1, wherein the plurality of time series are provided via one or more interfaces.

6. The computer-implemented method as claimed in claim 1, wherein the indexed time series sections are stored in a database or cloud.

7. The computer-implemented method as claimed in claim 1, wherein the numerical vector is a cardinal statistical label.

8. The computer-implemented method as claimed in claim 1, wherein the similarity search approach is a search method for searching for patterns based on similarity, or on dynamic time normalization (dynamic time warp, DTW).

9. The computer-implemented method as claimed in claim 1, further comprising performing at least one measure on a basis of the at least one identified time series section as output pattern, wherein the at least one measure is a measure selected from the group consisting of: displaying the output pattern on a display unit, the output pattern being displayed to a user; the user analyzing or processing the output pattern; selecting or filtering the output pattern from a plurality of the identified time series sections, taking account of the preceding analysis or processing by the user; transmitting the at least one output pattern to a computing unit for further analysis, further processing, further selection of further filtering by way of the computing unit; storing the output pattern in a storage unit, the storage unit being a volatile or non-volatile storage medium; analyzing or processing the output pattern; selecting or filtering the output pattern from a plurality of the identified time series sections; initiating a countermeasure on the basis of the analysis, the processing, the selection or the filtering; and providing an error message if no match or no similarity is detected.

10. A technical system for performing the computer-implemented method as claimed in claim 1.

11. A computer program product, comprising a computer readable hardware storage device having computer readable program code stored therein, said program code executable by a processor of a computer system to implement a method as claimed in claim 1 when the computer program is executed on a program-controlled device.

Description

BRIEF DESCRIPTION

[0062] Some of the embodiments will be described in detail, with reference to the following figures, wherein like designations denote like members, wherein:

[0063] FIG. 1 shows a flowchart for the method according to embodiments of the invention; and

[0064] FIG. 2 shows a cardinal statistical label according to an embodiment of the invention.

DETAILED DESCRIPTION

[0065] FIG. 1 schematically shows a flowchart for the method according to embodiments of the invention with the method steps S1 to S6.

[0066] Indexing the plurality of time series S1 to S3.

[0067] The plurality of time series are indexed, the time series being able to be stored in a time series database and being provided for indexing in a first step S1. As an alternative to the database, a different storage unit may be used, such as a cloud.

[0068] To this end, time series sections are initially generated from the time series S2, by applying the combination of statistical approaches or the machine learning model to the provided time series. The generated time series sections are indexed S3. Next, the key value indices are assigned S4.

[0069] The respective key value index is in the form of a numerical vector that denotes the respective time series section as a key and has the at least one position or one place in the respective time series as a value.

[0070] Illustrative key value indices are listed below.

Example 1

[0071] 0110101: 22, 34, 66

[0072] The key is 0110101 and accordingly a binary code. For each statistical approach in the combination of statistical approaches, a bit is displayed indicating whether the associated statistical approach has (1) or has not (0) detected an event.

[0073] In the 0110101 example, 7 statistical approaches form a combination of statistical approaches, 4 of the 7 approaches detecting an event and 3 of the 7 approaches not detecting an event. The majority of the statistical approaches therefore detect an event.

[0074] The value is 22, 34, 66 and is the position or place of the respective time series section in the time series.

Example 2

[0075] 1001110: 127, 883, 90

[0076] The key is 1001110 and accordingly a binary code. In the 1001110 example, 7 statistical approaches form a combination of statistical approaches, 4 of the 7 approaches detecting an event and 3 of the 7 approaches not detecting an event. The majority of the statistical approaches therefore detect an event.

[0077] The value is 127, 883, 90 and is the position or place of the respective time series section in the time series.

[0078] Searching the indexed time series sections for the input pattern S5, S6.

[0079] The indexing allows an efficient search for the input pattern to be performed on the indexed time series sections.

[0080] According to one embodiment, a range search (preliminary search) may initially be performed in order to generate a first set of search results. In other words, the range search looks for the input pattern in the form of the numerical vector, for the statistical label.

[0081] On the basis of that, a second search may be performed on this first set of search results by a more accurate or more specific method, DTW, in order to generate a second set of search results. In other words, the second search further limits the search results from the first search. The second search may also be referred to as a point search.

[0082] According to one embodiment of the invention, the index key in the form of the statistical label may be extended to form a cardinal statistical label, which is shown in FIG. 2. The cardinal statistical label records how often the respective statistical approach in the combination of statistical approaches has detected changes in the relevant period.

[0083] The cardinal statistical label is referred to as the second key and is recorded in the key value index. The preliminary search may be divided into two steps in this case, as follows:

[0084] In a first step, the statistical label is initially sought (key 1, binary statistical label). Then, in a second step, the cardinal statistical label is sought (key 2, cardinal statistical label). In other words, limiting is carried out in the second step, the limiting being carried out by way of a metric for the cardinal statistical label.

According to one embodiment, all results for which the distance from the cardinal statistical label predefined by the search does not exceed a certain magnitude are returned.

Example

[0085] Cardinal statistical label 1 (abbreviated to KSA 1)=2 1 2 0 0 2
Cardinal statistical label 2 (abbreviated to KSA 2)=1 1 3 0 0 1

Distance (KSL1, KSL2)=|2−1|+|1−1|+|2−3|+|0−0|+|0−0|+|2−1|=3

[0086] Although the present invention has been disclosed in the form of embodiments and variations thereon, it will be understood that numerous additional modifications and variations could be made thereto without departing from the scope of the invention.

[0087] For the sake of clarity, it is to be understood that the use of “a” or “an” throughout this application does not exclude a plurality, and “comprising” does not exclude other steps or elements.