A machine learning method related to these embodiments includes: first generating a pseudo attractor from each of plural series data sets, the pseudo attractor being a set of points in N-dimensional space, each of the points including N values sampled at an equal interval; second generating a series data set of Betti numbers from each of plural pseudo attractors generated in the first generating, each of the Betti numbers being a number of holes for a radius of a N-dimensional sphere in the N-dimensional space; and performing machine learning for each of plural series data sets of Betti numbers generated in the second generating, the series data set of Betti numbers being used as input in the machine learning.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A non-transitory computer-readable recording medium storing therein a learning program that causes a computer to execute a process comprising: first generating a pseudo attractor from each of a plurality of series data sets, the pseudo attractor being a set of points in N-dimensional space, each of the points including N values sampled at an equal interval; second generating a series data set of Betti numbers from each of a plurality of pseudo attractors generated in the first generating by calculation of persistent homology, each of the Betti numbers being a number of holes for a radius of a N-dimensional sphere in the N-dimensional space; and performing machine learning for each of a plurality of series data sets of Betti numbers generated in the second generating, the series data set of Betti numbers being used as input in the machine learning. 2. The non-transitory computer-readable recording medium as set forth in claim 1, wherein the second generating comprises: third generating data of duration between birth and death of holes for each hole dimension by the calculation of persistent homology; calculating the Betti numbers based on the data of duration for each hole dimension; and fourth generating the series data set of Betti numbers based on the Betti numbers calculated for each hole dimension. 3. The non-transitory computer-readable recording medium as set forth in claim 1, wherein each of the Betti numbers is a number of holes whose difference between a radius at birth and a radius at death is a predetermined length or more. 4. The non-transitory computer-readable recording medium as set forth in claim 1, further comprising: calculating an average of values included in the series data set for each of the plurality of series data sets, and wherein the performing comprises performing the machine learning, the series data set of Betti numbers and the average being used as input in the machine learning. 5. The non-transitory computer-readable recording medium as set forth in claim 1, wherein each of the plurality of series data sets is a labeled series data set, and the performing comprises performing the machine learning for a relationship between the Betti numbers for the radius of the N-dimensional sphere and a label. 6. The non-transitory computer-readable recording medium as set forth in claim 1, wherein the holes are elements of a homology group. 7. A machine learning method comprising: first generating, by using a computer, a pseudo attractor from each of a plurality of series data sets, the pseudo attractor being a set of points in N-dimensional space, each of the points including N values sampled at an equal interval; second generating, by using the computer, a series data set of Betti numbers from each of a plurality of pseudo attractors generated in the first generating by calculation of persistent homology, each of the Betti numbers being a number of holes for a radius of a N-dimensional sphere in the N-dimensional space; and performing, by using the computer, machine learning for each of a plurality of series data sets of Betti numbers generated in the second generating, the series data set of Betti numbers being used as input in the machine learning. 8. An information processing apparatus, comprising: a memory; and a processor coupled to the memory and configured to: first generate a pseudo attractor from each of a plurality of series data sets, the pseudo attractor being a set of points in N-dimensional space, each of the points including N values sampled at an equal interval; second generate a series data set of Betti numbers from each of a plurality of pseudo attractors generated in the first generating by calculation of persistent homology, each of the Betti numbers being a number of holes for a radius of a N-dimensional sphere in the N-dimensional space; and perform machine learning for each of a plurality of series data sets of Betti numbers generated in the second generating, the series data set of Betti numbers being used as input in the machine learning.