US 12,423,595 B2
Systems for time-series predictive data analytics, and related methods and apparatus
Jeremy Achin, Boston, MA (US); Thomas DeGodoy, Medford, MA (US); Xavier Conort, Singapore (SG); Mark L. Steadman, Watertown, MA (US); Peter Prettenhofer, Vienna (AT); and Timothy Owen, Newton, MA (US)
Assigned to DataRobot, Inc., Boston, MA (US)
Filed by DataRobot, Inc., Boston, MA (US)
Filed on Nov. 13, 2019, as Appl. No. 16/682,813.
Application 16/682,813 is a continuation of application No. 15/790,803, filed on Oct. 23, 2017, granted, now 10,496,927.
Application 15/790,803 is a continuation in part of application No. 15/331,797, filed on Oct. 21, 2016, granted, now 10,366,346, issued on Jul. 30, 2019.
Application 15/331,797 is a continuation in part of application No. 15/217,626, filed on Jul. 22, 2016, granted, now 9,652,714, issued on May 16, 2017.
Application 15/217,626 is a continuation of application No. 14/720,079, filed on May 22, 2015, granted, now 9,489,630, issued on Nov. 8, 2016.
Claims priority of provisional application 62/411,526, filed on Oct. 21, 2016.
Claims priority of provisional application 62/002,469, filed on May 23, 2014.
Prior Publication US 2020/0257992 A1, Aug. 13, 2020
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 5/04 (2023.01); G06F 9/50 (2006.01); G06N 5/02 (2023.01); G06N 5/046 (2023.01); G06N 20/00 (2019.01); G06N 20/10 (2019.01); G06N 20/20 (2019.01); G06Q 10/04 (2023.01); G06Q 10/06 (2023.01)
CPC G06N 5/04 (2013.01) [G06F 9/5011 (2013.01); G06N 5/02 (2013.01); G06N 5/046 (2013.01); G06N 20/00 (2019.01); G06N 20/10 (2019.01); G06N 20/20 (2019.01); G06Q 10/04 (2013.01); G06Q 10/06 (2013.01); Y02P 90/80 (2015.11)] 26 Claims
OG exemplary drawing
 
1. A computer-implemented modeling method comprising:
obtaining a plurality of data sets, wherein each data set includes a plurality of observations, wherein each observation includes an indication of a time of the observation and values of one or more variables, wherein at least one of the variables is a target;
setting values of a plurality of temporal parameters of a modeling procedure, the plurality of temporal parameters including a forecast range parameter and a skip range parameter, wherein the forecast range parameter indicates a duration of a period of time for prediction of one or more values of the target, and wherein the skip range parameter indicates a duration of a time period between a time of an earliest-in-time prediction in the forecast range and a time of a latest-in-time observation upon which the earliest-in-time prediction in the forecast range is based;
segmenting at least one of the data sets into training-input data and training-output data based, at least in part, on the values of the forecast range parameter and the skip range parameter, wherein the training-input data include a first subset of the observations of the at least one data set, the training-output data include a second subset of the observations of the at least one data set, a range of the times of the observations in the second subset matches the value of the forecast range parameter, and a range from a latest-in-time observation in the first subset to an earliest-in-time observation in the second subset matches the value of the skip range parameter; and
adapting a model to solve a prediction problem represented by the plurality of data sets and the values of the forecast range and skip range parameters, including training the model using the training-input data and the training-output data.