Sas Institute Inc.

Macklin Carter Frazier

Michael James Leonard

Li Xu

Yung-Hsin Chien

Michele Angelo Trovero

Jerzy Michal Brzezicki

Yue Li

Rajendra Singh Solanki

Rajib Nath

Phillip Mark Helmkamp

Jie Zhong

Jingrui Xie

Timothy Patrick Haley

Steven Christopher Mills

Kyungduck Cha

Ron Travis Hodgin

Randy Thomas Solomonson

Sangmin Kim

Time-series projections can be analyzed and manipulated via an interactive graphical user interface generated by a system. The graphical user interface can include a graph depicting an aggregated time-series projection (ATSP) over a future time. The ATSP can be generated by aggregating multiple time-series. The system can receive user input indicating that an existing value in the ATSP is to be overridden with an override value. In response, the system can adjust the ATSP using the override value to generate an updated version of the ATSP. The system can display the updated version of the ATSP in the graphical user interface. The system can also propagate the impact of overriding the existing value with the override value through the multiple time-series. The system can display an impact analysis portion within the graphical user interface indicating the impact of overriding the existing value with the override value on the multiple time-series.

Interactive graphical user-interface for analyzing and manipulating time-series projections

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

Macklin Carter Frazier