Abstract: A machine learning method includes acquiring data including attendance records of employees and information indicating which employee has taken a leave of absence from work, in response to determining that a first employee of the employees has not taken a leave of absence in accordance with the data, generating a first tensor on a basis of an attendance record of the first employee and parameters associated with elements included in the attendance record, in response to determining that a second employee of the employees has taken a leave of absence in accordance with the data, modifying the parameters, and generating a second tensor on a basis of an attendance record of the second employee and the modified parameters, and generating a model by machine learning based on the first tensor and the second tensor.

Abstract: A machine learning process including identifying a first axis including elements represented by one-hot vectors and a second axis including elements not represented by one-hot vectors, among a plurality of axes included in tensor-form data, when calculating a core tensor from the tensor-form data via a plurality of intermediate tensors, calculating the core tensor from the tensor-form data by a first process of concatenating elements included in a first element matrix corresponding to the first axis to elements included in a first intermediate tensor among the plurality of intermediate tensors and a second process of calculating a mode product between a second intermediate tensor among the plurality of intermediate tensors and a second element matrix corresponding to the second axis, and performing machine learning of a machine learning model with the core tensor as an input.

Abstract: A non-transitory computer-readable recording medium storing a model generation program for causing a computer to perform processing including: changing first data and generating a plurality of pieces of data; calculating a plurality of values indicating a distance between the first data and each of the plurality of pieces of data; determining whether or not a value indicating uniformity of distribution of the distance between the first data and each of the plurality of pieces of data is equal to or greater than a threshold based on the plurality of values; and in a case where the value indicating the uniformity is determined to be equal to or greater than the threshold, generating a linear regression model using a result obtained by inputting the plurality of pieces of data into a machine learning model as an objective variable and using the plurality of pieces of data as explanatory variables.

Abstract: A recording medium storing an explanatory program for causing a computer to execute an explanatory process. The process includes: generating a plurality of pieces of data based on first data; calculating a ratio of output results, among a plurality of results output in a case that each of the plurality of pieces of data is input to a machine learning model, different from first results output in a case that the first data is input to the machine learning model; generating a linear model based on the plurality of pieces of data and the plurality of results in a case that the calculated ratio satisfies a criterion; and outputting explanatory information with respect to the first results based on the linear model.

Abstract: A machine learning method is performed by a computer. The method includes acquiring first graph information, generating second graph information, without changing a coupling state between nodes included in the first graph information, by a change process of changing an attribute value of a coupling between the nodes, and performing machine learning on a model, based on the first graph information and the second graph information.

Abstract: A non-transitory computer-readable recording medium has stored therein a program that causes a computer to execute a process, the process including determining numerical values indicating features at respective timings having a predetermined time interval with respect to time-series data to be analyzed, numbers of the numerical values at the respective timings being made same, and generating an attractor related to the time-series data based on the determined numerical values.

Abstract: A non-transitory computer-readable recording medium stores therein an analysis program that causes a computer to execute a process including: dividing a Betti number sequence into a plurality of Betti number sequences, the Betti number sequence being included in a result of a persistent homology process performed on time series data, the plurality of Betti number sequences corresponding to different dimension of the Betti number sequence; and performing an analysis on each of the plurality of Betti number sequences.

Abstract: 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.

Abstract: A machine learning method includes acquiring data including attendance records of employees and information indicating which employee has taken a leave of absence from work, in response to determining that a first employee of the employees has not taken a leave of absence in accordance with the data, generating a first tensor on a basis of an attendance record of the first employee and parameters associated with elements included in the attendance record, in response to determining that a second employee of the employees has taken a leave of absence in accordance with the data, modifying the parameters, and generating a second tensor on a basis of an attendance record of the second employee and the modified parameters, and generating a model by machine learning based on the first tensor and the second tensor.

Abstract: 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.

Abstract: An extraction apparatus generates a plurality of Betti series based on Betti numbers obtained by performing a persistent homology transform on a plurality of pseudo-attractors generated from a plurality of pieces of time-series data. The extraction apparatus generates a plurality of transformed Betti series in which a region with a larger radius at the time of generating the Betti numbers is weighted more than a region with a smaller radius from the plurality of Betti series. The extraction apparatus extracts abnormality candidates from the plurality of pieces of time-series data based on the Betti number in the plurality of transformed Betti series.

Abstract: A non-transitory computer-readable recording medium stores therein an analysis program that causes a computer to execute a process including: dividing a Betti number sequence into a plurality of Betti number sequences, the Betti number sequence being included in a result of a persistent homology process performed on time series data, the plurality of Betti number sequences corresponding to different dimension of the Betti number sequence; and performing an analysis on each of the plurality of Betti number sequences.

Abstract: A non-transitory computer-readable recording medium stores therein a state classifying program that causes a computer to execute a process including: generating an attractor containing a plurality of points that correspond to a plurality of sets of time series data, coordinate values of each of the plurality of points being values corresponding to the sets of time series data; generating Betti number sequence data by applying a persistent homology process on the attractor; and classifying a state that is represented by the plurality of sets of time series data based on the Betti number sequence data.