Sas Institute Inc.

Steven Christopher Mills

Michele Angelo Trovero

Phillip Mark Helmkamp

Mahesh V Joshi

Yung-Hsin Chien

Jerzy Michal Brzezicki

Yue Li

Rajib Nath

Iman Vasheghani Farahani

Youngjin Park

Ron Travis Hodgin

Macklin Carter Frazier

Jingrui Xie

Timothy Patrick Haley

Ran Bi

Sangmin Kim

Vilochan Suresh Muley

Kritika Misra

Randy Thomas Solomonson

A system described herein can generate a graphical user interface (GUI) for a piece of forecasting software. The GUI can include graphical nodes arranged on a drag-and-drop canvas to define an overall forecasting pipeline. The graphical nodes can include a hierarchical modeling node that enables a user to define a time series hierarchy. The hierarchical modeling node can also enable separate level pipelines to be customized for each level of the time series hierarchy. The level pipelines can form subparts of the overall forecasting pipeline. The system can then execute the level pipelines to generate multiple forecasts, where each forecast corresponds to a respective level of the time series hierarchy. In some examples, the system can execute a reconciliation process on the forecasts to generate reconciled forecasts. Each reconciled forecast can correspond to one of the forecasts. The system may then generate one or more visualizations of the reconciled forecasts.

Hierarchical modeling node for visual forecasting

A pipeline system for time-series data forecasting using a distributed computing environment is disclosed herein. In one example, a pipeline for forecasting time series is generated. The pipeline represents a sequence of operations for processing the time series to produce modeling results such as forecasts of the time series. The pipeline includes a segmentation operation for categorizing the time series into multiple demand classes based on demand characteristics of the time series. The pipeline also includes multiple sub-pipelines corresponding to the multiple demand classes. Each of the sub-pipelines applies a model strategy to the time series in the corresponding demand class. The model strategy is selected from multiple candidate model strategies based on predetermined relationships between the demand classes and the candidate model strategies. The pipeline is executed to determine the modeling results for the time series.

Demand classification based pipeline system for time-series data forecasting

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Raleigh, NC, United States of America

Steven Christopher Mills