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Developing Time Series Models with the Python Machine Learning Client for SAP HANA

via SAP Learning

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Overview

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In this course, you will apply the machine learning workflow introduced in the enablement "Developing AI Models with the Python Machine Learning Client for SAP HANA". The steps from feature extraction to model evaluation are illustrated through the scenario of forecasting overnight stays in Switzerland. The course also demonstrates how to prepare data, configure the SAP HANA environment, and apply advanced forecasting techniques for both univariate and multivariate time series scenarios.

Syllabus

Understanding Machine Learning for Time Series Forecasting

Providing an Overview of Time Series Models
Exploring the Demo Scenario- Forecasting Overnight Stays
Understanding Machine Learning for Time Series Forecasting

Setting Up the Environment and Preparing Data Using SAP HANA DataFrames

Configuring the Python Machine-Learning Client for SAP HANA
Exploring and Manipulating Data with SAP HANA Dataframes
Setting Up the Environment and Preparing Data Using SAP HANA DataFrames

Training an SAP HANA PAL Univariate Time Series Model

Building a Univariate Forecasting Model with SAP HANA PAL
Training an SAP HANA PAL Univariate Time Series Model

Forecasting with Multiple Time Series

Preparing Data for Multiple Time Series Forecasting
Forecasting Across Grouped Time Series
Forecasting with Multiple Time Series

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