Sas Institute Inc.

International Business Machines Corporation

Sujatha Pothireddy

Jerzy Michal Brzezicki

Udo Sglavo

Michael James Leonard

Edward Tilden Blair

Rajib Nath

Vilochan Suresh Muley

Ranbir Singh Tomar

Kannukuzhiyil Kurien Kurien

Simon C Chu

Dean Alan Kalman

Richard Christopher Fore

Shawn M Austvold

Phillip Mark Helmkamp

Iman Vasheghani Farahani

Robert V Miller

Timothy Patrick Haley

Ron Travis Hodgin

Bhupendra Suresh Bendale

Robert Paul Rowe

Marty Eugene Turner

Neal Hale

Peter M Gaertner

Jim Evans

Daniel R Dahl

Marshall P Cline

Rebecca B Legler

Kritika Misra

Harshad Dinesh Bapat

Kaushal Lalitkumar Chauhan

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 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.

A method and system for indexing adapters within a data processing system where the data processing system contains multiple existing adapters, where each of the multiple existing adapters is identified by particular indexing data. All adapters within the data processing system are scanned. A determination of whether or not any additional adapters have been added to the data processing system in addition to the multiple existing adapters is made. Particular indexing data is assigned to any additional adapters in response to determining that additional adapters have been added to the data processing system, where any additional adapters added to the data processing system are indexed into the data processing system without changing the particular indexing data of each of the multiple existing adapters.

Method and system for indexing adapters within a data processing system

A framework for use with object-oriented programming systems includes one or more objects of a class called &ldquo;Socket&rdquo; that receive and process packets of work. As various applications execute, they require servicing of tasks, which correspond to the packets of work mentioned above. The packets of work are represented in the framework by objects of a class called &ldquo;WorkUnit&rdquo;. When a WorkUnit object is generated by an application, it in turn generates an object of a class called &ldquo;Retriever&rdquo; which is associated with the appropriate Socket object needed for servicing the WorkUnit. The Retriever object retrieves the Socket object to service the WorkUnit from an object of a class called &ldquo;SessionInfo&rdquo;. The SessionInfo object is a single object for maintaining registries of Socket objects and objects of a class called &ldquo;ApplicationInfo&rdquo;. ApplicationInfo objects contain application specific information that can be used, manipulated, and shared between one or more WorkUnits of a given application. WorkUnits of an application can retrieve ApplicationInfo objects from the SessionInfo object by use of a Retriever object which is associated with the appropriate ApplicationInfo object. The framework includes a core set of objects, including SessionInfo, Socket, ApplicationInfos, Retrievers, and WorkUnits, and permits user-defined, extensible instances of these objects that permit tailoring of tasks for particular needs.

Object oriented application program development framework mechanism

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Apex, NC, United States of America

Sujatha Pothireddy