公开(公告)号：US20210357401A1

公开(公告)日：2021-11-18

申请号：US16876463

申请日：2020-05-18

Applicant: BUSINESS OBJECTS SOFTWARE LTD.

Inventor： Paul O'Hara ,  Ying Wu ,  Esther Rodrigo Ortiz ,  Paul O'Connor ,  Gabor Szabo ,  Artur Stulka

IPC: G06F16/2458 ,  G06F16/23 ,  G06F17/18

Abstract: The present disclosure involves systems, software, and computer implemented methods for automatically recommending one or more frequencies for time series data. One example method includes receiving a request for an insight analysis for an input time series included in a dataset. For each of multiple frequencies to analyze, the input time series is transformed into a frequency time series. An absolute percentage change impact factor and an absolute trend impact factor are determined for each frequency time series. A frequency interest score is determined based on the determined absolute percentage change factors and the determined absolute trend impact factors, for each time frequency time series. The frequency interest score is provided for at least some of the frequency time series.

公开(公告)号：US20200098055A1

公开(公告)日：2020-03-26

申请号：US16140760

申请日：2018-09-25

Applicant: BUSINESS OBJECTS SOFTWARE LTD.

Inventor： Paul O'Hara ,  Ying Wu

IPC: G06Q40/00 ,  G06Q30/02 ,  G06Q10/06 ,  G06N5/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, to predict future Day Sales Outstanding (DSO) forecasts for a number of future time periods. In one aspect, a method includes receiving open receivables financial line item data and revenue financial line item data, providing the open receivables financial line item data to a DSO predictor engine to generate a predicted open receivables that includes a multi-step time series forecasting regression generated from the open receivables financial line item data, providing the revenue financial line item data to the DSO predictor engine to generate a predicted revenue comprising the multi-step time series forecasting regression generated from the revenue financial line item data; generating a predicted DSO with the predicted open receivables and predicted revenue, and providing the predicted DSO to a client device.

公开(公告)号：US10248713B2

公开(公告)日：2019-04-02

申请号：US15364681

申请日：2016-11-30

Applicant: Business Objects Software Ltd.

Inventor： Paul Pallath ,  Ying Wu

IPC: G06F17/30 ,  G06N99/00

Abstract: Techniques are described for performing a time series analysis using a clustering based symbolic representation. Implementations employ a clustering based symbolic representation applied to time series data. In some implementations, the time series data is discretized into subsequences with regular time intervals, and symbols encoding the time intervals may be derived by performing clustering algorithms on the subsequences. In the new representation, a time series is transformed into a sequence of categorical values. The symbolic representation is suitable to perform time series classification and forecast with higher accuracy and greater efficiency compared to previously used techniques. Through use of the symbolic representation, a dimension reduction is applied to transform the time sequences to a feature space with lower dimensions. As output of such transformation, a new representation is obtained based on the original time series. This new reduced-dimension representation improves the efficiency of time series data mining and forecasting.

公开(公告)号：US20180150547A1

公开(公告)日：2018-05-31

申请号：US15364681

申请日：2016-11-30

Applicant: Business Objects Software Ltd.

Inventor： Paul Pallath ,  Ying Wu

IPC: G06F17/30 ,  G06N99/00

CPC classification number: G06F17/30598 ,  G06F17/30539 ,  G06N99/005

Abstract: Techniques are described for performing a time series analysis using a clustering based symbolic representation. Implementations employ a clustering based symbolic representation applied to time series data. In some implementations, the time series data is discretized into subsequences with regular time intervals, and symbols encoding the time intervals may be derived by performing clustering algorithms on the subsequences. In the new representation, a time series is transformed into a sequence of categorical values. The symbolic representation is suitable to perform time series classification and forecast with higher accuracy and greater efficiency compared to previously used techniques. Through use of the symbolic representation, a dimension reduction is applied to transform the time sequences to a feature space with lower dimensions. As output of such transformation, a new representation is obtained based on the original time series. This new reduced-dimension representation improves the efficiency of time series data mining and forecasting.