Sas Institute Inc.

Udo Sglavo

Michael James Leonard

Sujatha Pothireddy

Jerzy Michal Brzezicki

Edward Tilden Blair

Rajib Nath

Vilochan Suresh Muley

Ranbir Singh Tomar

Kannukuzhiyil Kurien Kurien

Susan Edwards Haller

Michael Ryan Chipley

Wendy Ann Czika

Phillip Mark Helmkamp

Sagar Arun Mainkar

Dinesh P Apte

Timothy Patrick Haley

Kshitija Ambulgekar

Ashwini Bhalchandra Dixit

Sarika Shrotriya

Funda Gunes

A computing device selects a feature set and hyperparameters for a machine learning model to predict a value for a characteristic in a scoring dataset. A number of training model iterations is determined. A unique evaluation pair is selected for each iteration that indicates a feature set selected from feature sets and a hyperparameter configuration selected from hyperparameter configurations. A machine learning model is trained using each unique evaluation pair. Each trained machine learning model is validated to compute a performance measure value. An estimation model is trained with the feature set, the hyperparameter configuration, and the performance measure value computed for unique evaluation pair. The trained estimation model is executed to compute the performance measure value for each unique evaluation pair. A final feature set and a final hyperparameter configuration are selected based on the computed performance measure value.

System for automatic, simultaneous feature selection and hyperparameter tuning for a machine learning model

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 analyzing unstructured time stamped data. A first series of user display screens are provided, where the first series of user display screens are configured to be displayed in a step-wise manner so that a user can specify a first approach through a series of predetermined steps on how the unstructured data is to be structured. A second series of user display screens are provided, where the second series of user display screens are configured to be displayed in a step-wise manner so that the user can specify a second approach through the series of predetermined steps on how the unstructured data is to be structured. Tracking data enables alternate viewing of the first and second approach to facilitate a decision whether to format the unstructured time stamped data according to the first approach or the second approach.

Computer-implemented systems and methods for time series exploration using structured judgment

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Raleigh, NC, United States of America

Udo Sglavo