You're reading from Codeless Time Series Analysis with KNIME A practical guide to implementing forecasting models for time series analysis applications

Product type Paperback

Published in Aug 2022

Publisher Packt

ISBN-13 9781803232065

Length 392 pages

Edition 1st Edition

Languages

Java

Tools

Knime

Concepts

Data Analysis

Authors (4):

Daniele Tonini

Maarit Widmann

Corey Weisinger

KNIME AG

View More author details

Table of Contents (20) Chapters

Preface

1. Part 1: Time Series Basics and KNIME Analytics Platform

2. Chapter 1: Introducing Time Series Analysis FREE CHAPTER

3. Chapter 2: Introduction to KNIME Analytics Platform

4. Chapter 3: Preparing Data for Time Series Analysis

5. Chapter 4: Time Series Visualization

6. Chapter 5: Time Series Components and Statistical Properties

7. Part 2: Building and Deploying a Forecasting Model

8. Chapter 6: Humidity Forecasting with Classical Methods

9. Chapter 7: Forecasting the Temperature with ARIMA and SARIMA Models

10. Chapter 8: Audio Signal Classification with an FFT and a Gradient-Boosted Forest

11. Chapter 9: Training and Deploying a Neural Network to Predict Glucose Levels

12. Chapter 10: Predicting Energy Demand with an LSTM Model

13. Chapter 11: Anomaly Detection – Predicting Failure with No Failure Examples

14. Part 3: Forecasting on Mixed Platforms

15. Chapter 12: Predicting Taxi Demand on the Spark Platform

16. Chapter 13: GPU Accelerated Model for Multivariate Forecasting

17. Chapter 14: Combining KNIME and H2O to Predict Stock Prices

18. Answers

19. Other Books You May Enjoy

Recapping regression

If you’re reading this book, without a doubt, you’ve heard of regression, and maybe you’re even quite versed in its application, but humor me for a moment all the same. I find a quick aside from this topic to be very helpful when framing a conversation about the (S)ARIMA models. In the following diagram, you can see a linear regression line fit through some data points. We’ll start by recapping this concept and then expand into the ARIMA and SARIMA models from there:

Figure 7.1 – A regression line fit in KNIME Analytics Platform

The line fit in the preceding regression is completely straight. We need the ability to capture seasonal patterns, cycles, and other auto-regressive features our time series data might contain. This is where the expansion into the ARIMA and SARIMA models will take us. Let’s define this a bit more.