Introduction

Overview

Questions

• How do you build machine learning pipelines for time-series analysis?

Objectives

• Introduce machine learning concepts applicable to time-series forecasting.
• Introduce Google’s TensorFlow machine learning library for Python.

Introduction

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This lesson is the third in a series of lessons demonstrating Python libraries and methods for time-series analysis and forecasting.

The first lesson, Time Series Analysis of Smart Meter Power Consmption Data, introduces datetime indexing features in the Python Pandas library. Other topics in the lesson include grouping data, resampling by time frequency, and plotting rolling averages.

The second lesson, Modeling Power Consumption with Python, introduces the component processes of the SARIMAX model for single-step forecasts based on a single variable.

This lesson builds upon the first two by applying machine learning processes to build models with potentially greater predictive power against larger datasets. Relevant concepts include:

• Feature engineering
• Data windows
• Single step forecasts
• Multi-step forecasts

Throughout, the lesson uses Google’s TensorFlow machine learning library and the related Python API, keras. As noted in each section of the lesson, the code is based upon and is in many cases a direct implementation of the Time series forecasting tutorial available from the TensorFlow project. Per the documentation, materials available from the TensorFlow GitHub site are published using an Apache 2.0 license.

Google Inc. (2023) TensorFlow Documentation. Retrieved from https://github.com/tensorflow/docs/blob/master/README.md.

This lesson uses the same dataset as the previous two. For more information about the data, see the Setup page.

Key Points

• The TensorFlow machine learning library from Google provides many algorithms and models for efficient pipelines to process and forecast large time-series datasets.