Smart-EDGAR is supporting Formulas now…

Published by pschatzmann on

In the last Blog we demonstrated how we can calculate KPIs with the help of Spark.

We have extended Smart Edgar functionality so that we can implement Calculated KPIs directly with the help of formulas. Here is a short demo in Scala which uses

  • formulas
  • the built in ‘coalesce’ method
  • the built in ‘lag’ method
  • the built in ‘percentChange’ method

and displays the result in a BeakerX Jupyter Notebook as tables and charts using the Java API.

This functionality is also available as webservice

see also:

  1. Deeplearning4j – Recurrent Neural Networks (RNN)
  2. Deeplearning4j – Iris Classification Example
  3. Solid Modelling for 3D Printing for the JVM in Jupyterlab
  4. H2O Sparkling Water – Distributed Random Forrest
  5. NFS in Docker-Compose
  6. H2O Deployment in Scala, Groovy, Kotlin….
  7. Identity Management and Authentication for Java Webservices and Javascript GUIs
  8. An OpenSCAD Kernel for Jupyter
  9. Random Forrest Classifier in Spark ML
  10. Using Shogun in the JVM
Categories: Data ScienceEDGAR

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