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Tutorial 2A: Building, interpreting, and scoring time series models with H2O Driverless AI

Introduction​

Overview​

In this tutorial, we will learn how to build, interpret, and score a time series model in H2O Driverless AI.

A time series model is a predictive model that leverages machine learning algorithms to analyze and forecast data points in a temporal sequence. Unlike traditional statistical models, time series models can capture complex patterns and relationships within the data by learning from large amounts of historical data. These models can handle non-linear patterns and interactions between variables and even incorporate external data sources.

Time series models are utilized in various domains, such as finance (stock price prediction), healthcare (patient monitoring), retail (sales forecasting), and more. They offer the advantage of handling complex and high-dimensional data for more accurate and robust predictions.

Throughout the tutorial, we will harness a synthesized version of the renowned dataset, Walmart Recruiting—Store Sales Forecasting, to construct a time series regression model that predicts the sales for several departments in 45 Walmart stores in different regions. We will also learn how to generate a machine learning interpretability (MLI) report for a time series model in H2O Driverless AI.

The MLI report is particularly valuable for enhancing machine learning interpretability, especially in regulated industries where transparency and explanation are crucial. With a model's MLI report, you can uncover valuable insights into its inner workings, such as feature importance and other performance metrics. This report can help you better understand and trust the predictions made by the model, offering deeper insights into your built model and enabling you to make informed decisions in machine learning applications with the model.

Objectives​

  • Time series model: Learn how to build and score a time series model in H2O Driverless AI.
  • Time series model datasets: Learn how to prepare the training and test dataset for a time series model in H2O Driverless AI.
  • MLI report for a time series model: Learn how to generate and explore an MLI report for a time series model in H2O Driverless AI.

Prerequisites​

  • Access to Lab 23 in Aquarium containing H2O Driverless AI v1.10.7 (LTS)
    note

    To learn how to access Lab 23 in Aquarium, see Access an Aquarium lab.

  • Familiarity with H2O Driverless AI is essential. Alternatively, completion of the following tutorial is also acceptable: Tutorial 1A: Introduction to H2O Driverless AI.
  • Basic knowledge of Machine Learning and Statistics
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