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.

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, ?} including modeling information Z and error information ? 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, ?} as an input.