Use case: You want to predict the next value in a Time Series Sequence with Multilayer Perceptron (MLP).
This use case demonstrates the implementation of a Multilayer Perceptron (MLP) using TensorFlow and Keras for predicting the next value in a time series sequence. This predictive model can be valuable in various scenarios such as financial forecasting, stock price prediction, or any sequential data prediction task.
For this use case, assume:
- The keras python script "keras.py" is stored on client’s machine.
- The requirement file “reqs_keras.txt” is stored on client’s machine.
- The csv file “ex2data.csv“ is stored on client’s machine.
Prerequisite steps:
- Connect from the client to a target VantageCloud Lake system where the simulation task will be performed, as illustrated in the Introduction.
- Import necessary modules for this use case.
from teradatasqlalchemy.types import VARCHAR
- Specify the path where the script files are kept on the client.This is specified as a variable to avoid repetitive typing.
path_to_files = '/Users/JaneDoe/OpeanAF_examples/scripts/'