Sas Institute Inc.

Iman Vasheghani Farahani

Mahesh V Joshi

Michele Angelo Trovero

Rajib Nath

Phillip Mark Helmkamp

Youngjin Park

Sujatha Pothireddy

Vilochan Suresh Muley

Javier Delgado

Bhupendra Suresh Bendale

Ron Travis Hodgin

Steven Christopher Mills

Ran Bi

Kritika Misra

Sounak Paul

Harshad Dinesh Bapat

Kaushal Lalitkumar Chauhan

A system and method include tuning hyperparameters for an ARIMA model using a derivative free approach by determining a set of initial hyperparameter values, fitting an ARIMA model to the set of initial hyperparameter values, selecting a tuning method for the set of hyperparameters, responsive to selecting a single-objective method, computing a first objective function value from time-series data applied to the ARIMA model based on the set of initial hyperparameter values, or responsive to selecting a multi-objective method, computing at least a second objective function value and a third objective function value from the time-series data applied to the ARIMA model based on the set of initial hyperparameter values, determining whether a stopping criterion for tuning the set of hyperparameters has reached, responsive to determining that the stopping criteria has reached, outputting a set of tuned hyperparameter values.

Hyperparameter tuning in autoregressive integrated moving average (ARIMA) models

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

In one example, a computer system can generate a graphical user interface (GUI) for forecasting software including a drag-and-drop canvas with a set of rearrangeable nodes defining a forecasting pipeline. The computer system can detect a user interaction for attaching an external-language execution node to the pipeline, which can be used to insert custom code defined using an external programming language. The computer system can receive the custom code. The computer system can receive a user input to initiate execution of the pipeline. The computer system can generate wrapped custom code by augmenting the custom code with additional program code including shared variables. The computer system can provide the wrapped custom code to a set of execution threads configured to execute the wrapped custom code as part of the pipeline to generate one or more forecasts. The computer system can output the forecasts in the GUI.

External-language execution node for visual forecasting

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

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Charlotte, NC, United States of America

Iman Vasheghani Farahani