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Tims_​RNN_​Keras_​Deployment

Multivariate Time Series Analysis with an RNN - Deployment

This is a simple example workflow for the deployment of a multivariant time series, LSTM based, recurrent neural network.

It is based on the bike demand predition dataset from Kaggle and uses the trained model to predict the demand in the next hour based on the demand and the other features in the last 10 hours.



Read and preprocess new data Apply the deep learningmodel to predict thedemand in the next hour Denormalize and roundthe predicted value Read the trained deep learning model and the normalization model Day of monthDay of weekMonth HourRead trainednetworkRead normalizer modelNode 208Node 209Node 210 Extract Date Fields Keras NetworkReader Model Reader Restructure Data Normalizer (Apply) Keras NetworkExecutor Denomrmalize Predictionsand Round to Int Excel Writer Excel Reader Excel Writer Read and preprocess new data Apply the deep learningmodel to predict thedemand in the next hour Denormalize and roundthe predicted value Read the trained deep learning model and the normalization model Day of monthDay of weekMonth HourRead trainednetworkRead normalizer modelNode 208Node 209Node 210 Extract Date Fields Keras NetworkReader Model Reader Restructure Data Normalizer (Apply) Keras NetworkExecutor Denomrmalize Predictionsand Round to Int Excel Writer Excel Reader Excel Writer

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Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: kathrinmelcher
Created at: 2020-10-13
On NodePit since: 2023-02-26
Last update: 2024-04-27
Created with KNIME version: v4.7.1
Tags: time seriesdeep learningRNNLSTMmultivariate time seriesmanufacturing

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