[jira] [Commented] (SPARK-10409) Multilayer perceptron regression

["Angel Conde (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-10409?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16500278#comment-16500278 ] 

Angel Conde  commented on SPARK-10409:
--------------------------------------

Another important use case for the MLP regressor is for forecasting machine sensor data. One of our clients uses this approach for predictive maintenance on Industry 4.0. assets. We were hoping to replace their custom implementation using ad-hoc library with Spark ML implementation but we are blocked until this get merged.

Can't go into too much detail about the use case, but it's in production in industrial environments. The general approach is to predict one sensor value based on others.

> Multilayer perceptron regression
> --------------------------------
>
>                 Key: SPARK-10409
>                 URL: https://issues.apache.org/jira/browse/SPARK-10409
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML
>    Affects Versions: 1.5.0
>            Reporter: Alexander Ulanov
>            Priority: Minor
>
> Implement regression based on multilayer perceptron (MLP). It should support different kinds of outputs: binary, real in [0;1) and real in [-inf; +inf]. The implementation might take advantage of autoencoder. Time-series forecasting for financial data might be one of the use cases, see http://dl.acm.org/citation.cfm?id=561452. So there is the need for more specific requirements from this (or other) area.



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