Sas Institute Inc.

Jerzy Michal Brzezicki

Michael James Leonard

Edward Tilden Blair

Sujatha Pothireddy

Udo Sglavo

Rajib Nath

Phillip Mark Helmkamp

Vilochan Suresh Muley

Kannukuzhiyil Kurien Kurien

Ranbir Singh Tomar

Timothy Patrick Haley

Yung-Hsin Chien

Michele Angelo Trovero

Michael Ryan Chipley

Yue Li

Sagar Arun Mainkar

Steven Christopher Mills

Macklin Carter Frazier

Ron Travis Hodgin

Dinesh P Apte

Jingrui Xie

Sangmin Kim

Randy Thomas Solomonson

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

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 forecasts. The sequence of operations include model strategy operations for applying various model strategies to the time series to determine error distributions corresponding to the model strategies. The sequence of operations further include a model-strategy comparison operation for determining which of the model strategies is a champion model strategy for the plurality of time series based on the error distributions of the model strategies. The pipeline is executed to determine the champion model strategy for the time series.

Pipeline system for time-series data forecasting

Systems and methods are provided for analyzing unstructured time stamped data. A distribution of time-stamped data is analyzed to identify a plurality of potential time series data hierarchies for structuring the data. An analysis of a potential time series data hierarchy may be performed. The analysis of the potential time series data hierarchies may include determining an optimal time series frequency and a data sufficiency metric for each of the potential time series data hierarchies. One of the potential time series data hierarchies may be selected based on a comparison of the data sufficiency metrics. Multiple time series may be derived in a single-read pass according to the selected time series data hierarchy. A time series forecast corresponding to at least one of the derived time series may be generated.

Computer-implemented systems and methods for time series exploration

Systems and methods are provided for analyzing unstructured time stamped data of a physical process in order to generate structured hierarchical data for a hierarchical time series analysis application. A plurality of time series analysis functions are selected from a functions repository. Distributions of time stamped unstructured data are analyzed to identify a plurality of potential hierarchical structures for the unstructured data with respect to the selected time series analysis functions. Different recommendations for the potential hierarchical structures for each of the selected time series analysis functions are provided, where the selected time series analysis functions affect what types of recommendations are to be provided, and the unstructured data is structured into a hierarchical structure according to one or more of the recommended hierarchical structures, where the structured hierarchical data is provided to an application for analysis using one or more of the selected time series analysis functions.

Systems and methods are provided for evaluating performance of forecasting models. A plurality of forecasting models may be generated using a set of in-sample data. Two or more forecasting models from the plurality of forecasting models may be selected for use in generating a combined forecast. An ex-ante combined forecast may be generated for an out-of-sample period using the selected two or more forecasting models. The ex-ante combined forecast may then be compared with a set of actual out-of-sample data to evaluate performance of the combined forecast.

Computer-implemented systems and methods for testing large scale automatic forecast combinations

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Cary, NC, United States of America

Jerzy Michal Brzezicki