公开(公告)号：US20230229980A1

公开(公告)日：2023-07-20

申请号：US18021839

申请日：2021-06-30

Applicant: OSAKA UNIVERSITY

Inventor： Koki KAWABATA ,  Yasuko SAKURAI ,  Takato HONDA ,  Yasushi SAKURAI

IPC: G06Q10/04 ,  G06F17/16

CPC classification number: G06Q10/04 ,  G06F17/16

Abstract: A forecasting apparatus forecasts an event after a predetermined time, based on a current window being a part of time-series data in multidimension. The forecasting apparatus includes a non-linear transformation unit including a matrix for non-linear transformation, an observation matrix, and a seasonality setting unit. The non-linear transformation unit transforms the time-series data of the current window in a part of dimensions that are related to trends and the time-series data of the current window in a part of dimensions that are related to seasonal intensity into latent first data showing the trends and latent second data showing the seasonal intensity. The observation matrix includes a first observation matrix that reproduces the first data to first estimated data of an original number of dimensions, and a second observation matrix that, by use of seasonality information that has been set in the seasonality setting unit, reproduces the second data to second estimated data of an original number of dimensions, and adds the first estimated data and the second estimated data.

公开(公告)号：US20230058585A1

公开(公告)日：2023-02-23

申请号：US17793388

申请日：2021-01-12

Applicant: OSAKA UNIVERSITY

Inventor： Takato HONDA ,  Yasuko SAKURAI ,  Koki KAWABATA ,  Yasushi SAKURAI

IPC: G06N3/04

Abstract: An event forecasting system includes a feature amount extracting unit and a forecasting unit. The feature amount extracting unit continuously extracts model parameters {m, r, S, ⊝, F} of dynamic patterns in a time direction and a facility direction from a multidimensional time-series tensor X of time-series sensor data collected for every period n from a plurality of types d of sensors respectively disposed at a plurality w of facilities of a factory, and further sequentially featurizes the multidimensional time-series tensor X into summary information {Z, ε} including modeling information Z and error information ε of the modeling information by use of the model parameter {m, r, S, ⊝, F}. The forecasting unit outputs a probability p of occurrence of an alert label y at a predetermined time Is ahead by use of the summary information {Z, ε} as an input.