Sas Institute Inc.

Ron Travis Hodgin

Yung-Hsin Chien

Michele Angelo Trovero

Sujatha Pothireddy

Jerzy Michal Brzezicki

Yue Li

Phillip Mark Helmkamp

Iman Vasheghani Farahani

Macklin Carter Frazier

Steven Christopher Mills

Timothy Patrick Haley

Jingrui Xie

Bhupendra Suresh Bendale

Kaushal Lalitkumar Chauhan

Randy Thomas Solomonson

Kritika Misra

Sangmin Kim

Harshad Dinesh Bapat

Some examples can involve a system that can receive a first user selection of time series data and a second user selection of a type of forecasting model to apply to the time series data. The system can then obtain a first set of candidate values and a second set of candidate values for a first parameter and a second parameter, respectively, of the selected type of forecasting model. The candidate values may be determined based on statistical information derived from the time series data. The system can then provide the first set of candidate values and the second set of candidate values to the user, receive user selections of a first parameter value and a second parameter value, and determine whether a conflict exists between the first parameter value and the second parameter value. If so, the system can generate an output indicating that the conflict exists.

Computerized engines and graphical user interfaces for customizing and validating forecasting models

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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Roxboro, NC, United States of America

Ron Travis Hodgin