Computer-readable recording medium, detection method, and detection device
A detection device adds, with regard to each of a plurality of sets of time-series data including a plurality of items, a time-shift term to at least any of the plurality of items included in each of the plurality of sets of time-series data. The detection device generates a plurality of attractors from the plurality of sets of time-series data to which the time-shift term has been added. The detection device generates a plurality of Betti sequences from each of the plurality of attractors by executing a persistent homology transformation on each of the plurality of attractors, each of the plurality of Betti sequences indicating a correspondence relationship between a Betti number and a scale value has been used for the persistent homology transformation. The detection device detects a state change in the plurality of sets of time-series data based on a time change in the plurality of Betti sequences.
1. A non-transitory computer-readable recording medium having stored therein a program that causes a computer to execute a process, the process comprising:
adding, with regard to each of a plurality of sets of time-series data including a plurality of items, a time-shift term to at least any of the plurality of items included in each of the plurality of sets of time-series data;
generating a plurality of attractors from the plurality of sets of time-series data to which the time-shift term has been added;
generating a plurality of Betti sequences from each of the plurality of attractors by executing a persistent homology transformation on each of the plurality of attractors, each of the plurality of Betti sequences indicating a correspondence relationship between a Betti number and a scale value has been used for the persistent homology transformation; and
detecting a state change in the plurality of sets of time-series data based on a time change in the plurality of Betti sequences.
2. The non-transitory computer-readable recording medium according to claim 1 , the process further comprising:
generating an attractor from any of the sets of plurality of time-series data;
generating an additional attractor from time-series data to which the time-shift term has been added; and
detecting the state change based on a Betti sequence generated from the attractor and a Betti sequence generated from the additional attractor.
3. The non-transitory computer-readable recording medium according to claim 1 , the process further comprising:
determining a number of the plurality of time-shift terms for each of the plurality of items;
generating a plurality of attractors from each of the plurality of sets of time-series data to which the determined time-shift term has been added;
generating a plurality of Betti sequences from each of the plurality of attractors by executing the persistent homology transformation; and
detecting the state change in the plurality of sets of time-series data based on a time change in the plurality of Betti sequences.
4. A detection method comprising:
adding, with regard to each of a plurality of sets of time-series data including a plurality of items, a time-shift term to at least any of the plurality of items included in each of the plurality of sets of time-series data, using a processor;
generating a plurality of attractors from the plurality of sets of time-series data to which the time-shift term has been added, using the processor;
generating a plurality of Betti sequences from each of the plurality of attractors by executing a persistent homology transformation on each of the plurality of attractors, each of the plurality of Betti sequences indicating a correspondence relationship between a Betti number and a scale value has been used for the persistent homology transformation, using the processor; and
detecting a state change in the plurality of sets of time-series data based on a time change in the plurality of Betti sequences, using the processor.
5. A detection device comprising:
a memory; and
a processor coupled to the memory and the processor configured to:
add, with regard to each of a plurality of sets of time-series data including a plurality of items, a time-shift term to at least any of the plurality of items included in each of the plurality of sets of time-series data;
generate a plurality of attractors from the plurality of sets of time-series data to which the time-shift term has been added;
generate a plurality of Betti sequences from each of the plurality of attractors by executing a persistent homology transformation on each of the plurality of attractors, each of the plurality of Betti sequences indicating a correspondence relationship between a Betti number and a scale value has been used for the persistent homology transformation; and
detect a state change in the plurality of sets of time-series data based on a time change in the plurality of Betti sequences.