公开(公告)号：US20230122150A1

公开(公告)日：2023-04-20

申请号：US17731147

申请日：2022-04-27

Applicant: Oracle International Corporation

Inventor： Nitin Rawat ,  Lakshmi Sirisha Chodisetty ,  Samik Raychaudhuri ,  Vijayalakshmi Krishnamurthy

IPC: G06N7/00 ,  G06K9/62

Abstract: Techniques are described for providing explanation information for time series-based predictions made using statistical models, such as linear statistical models, examples of which include various Exponential Smoothing models, Autoregressive Integrated Moving Average (ARIMA) models, and others. For a forecast predicted by a statistical model that has been trained upon and/or fit to a set of historical times series data points, an explanation is generated for the forecast, where the explanation for the forecast includes information indicative of the importance or impact or influence of individual time series data points in the set on the forecast. The explanation for the forecast may be output to a user along with the forecast. This enables the user to have some visibility into why the particular forecast was predicted by the statistical model.

公开(公告)号：US20230123573A1

公开(公告)日：2023-04-20

申请号：US17861634

申请日：2022-07-11

Applicant: Oracle International Corporation

Inventor： Chirag Ahuja ,  Samik Raychaudhuri ,  Anku Kumar Pandey ,  Nitin Rawat

IPC: G06F17/18 ,  G06F16/2458 ,  G06F17/14

Abstract: The present embodiments relate to generating input parameters for selecting a forecasting model. An example method includes a computing device receiving a time series comprising a plurality of data points, wherein each data point of the time series comprises a time associated with the data point and a value. The device can identify a first season and a second season from the time series, wherein a length of the first season is a factor of a length of the second season. The device can estimate a Fourier order and a seasonality mode for the first season based at least in part on the length of the first season and the length of the second season. The device can select a forecasting model to forecast a value of a future time step of the time series based at least in part on the Fourier order and the seasonality mode.