An information processing apparatus (900, 2110) of abnormality diagnosis of a bearing, comprising: an acquisition means (910, 2120) that acquires measured data relating to vibrations of the bearing; a determination means (920, 2130) that determines, based on the measured data acquired by the acquisition means, a revision coefficient being a coefficient in a revised autoregressive model; and a diagnosis means (930, 2140) that diagnoses an abnormality of the bearing based on the revision coefficient determined by the determination means; and an output means (950,2150) that outputs information relating to the result of the diagnosis by the diagnosis means, wherein the revised autoregressive model is an equation expressing a predicted value of the measured data by using an actual value of the measured data and the revision coefficient in response to the actual value, the determination means determines the revision coefficient by using an equation in which a first matrix is set to a coefficient matrix and an autocorrelation vector derived from the measured data is set to a constant vector, wherein the first matrix is derived from a diagonal matrix and an orthogonal matrix, the diagonal matrix is a matrix whose diagonal component is eigenvalues of an autocorrelation matrix, the eigenvalues being derived by subjecting the autocorrelation matrix derived from the measured data to singular value decomposition, and the orthogonal matrix is a matrix in which an eigenvector of the autocorrelation matrix is set to a column component, the autocorrelation vector is a vector whose component is autocorrelation of the measured data with a time lag of 1 to m, m being a number of the actual value used in the revised autoregressive model, the autocorrelation matrix is a matrix whose component is autocorrelation of the measured data with a time lag of 0 to m - 1, the first matrix is a matrix U S Σ S U S T that is derived from a second matrix Σ S derived from s pieces of the eigenvalues of the autocorrelation matrix, s being a number set to 1 or more and less than m, and the diagonal matrix and a third matrix U s derived from s pieces of the eigenvalues and the orthogonal matrix, the second matrix is a submatrix of the diagonal matrix and is a matrix whose diagonal component is s pieces of the eigenvalues, and the third matrix is a submatrix of the orthogonal matrix and is a matrix in which eigenvectors corresponding to s pieces of the eigenvalues are set to a column component vector. an acquisition means (910, 2120) that acquires measured data relating to vibrations of the bearing; a determination means (920, 2130) that determines, based on the measured data acquired by the acquisition means, a revision coefficient being a coefficient in a revised autoregressive model; and a diagnosis means (930, 2140) that diagnoses an abnormality of the bearing based on the revision coefficient determined by the determination means; and an output means (950,2150) that outputs information relating to the result of the diagnosis by the diagnosis means, wherein the revised autoregressive model is an equation expressing a predicted value of the measured data by using an actual value of the measured data and the revision coefficient in response to the actual value, the determination means determines the revision coefficient by using an equation in which a first matrix is set to a coefficient matrix and an autocorrelation vector derived from the measured data is set to a constant vector, wherein the first matrix is derived from a diagonal matrix and an orthogonal matrix, the diagonal matrix is a matrix whose diagonal component is eigenvalues of an autocorrelation matrix, the eigenvalues being derived by subjecting the autocorrelation matrix derived from the measured data to singular value decomposition, and the orthogonal matrix is a matrix in which an eigenvector of the autocorrelation matrix is set to a column component, the autocorrelation vector is a vector whose component is autocorrelation of the measured data with a time lag of 1 to m, m being a number of the actual value used in the revised autoregressive model, the autocorrelation matrix is a matrix whose component is autocorrelation of the measured data with a time lag of 0 to m - 1, the first matrix is a matrix U S Σ S U S T that is derived from a second matrix Σ S derived from s pieces of the eigenvalues of the autocorrelation matrix, s being a number set to 1 or more and less than m, and the diagonal matrix and a third matrix U s derived from s pieces of the eigenvalues and the orthogonal matrix, the second matrix is a submatrix of the diagonal matrix and is a matrix whose diagonal component is s pieces of the eigenvalues, and the third matrix is a submatrix of the orthogonal matrix and is a matrix in which eigenvectors corresponding to s pieces of the eigenvalues are set to a column component vector.