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

Time granularity and time aggregation

In this section, we will introduce the concepts of time granularity and time aggregation. We will show examples of time series with different granularities. Additionally, we will show you how to aggregate time series in KNIME. We will cover these topics in the following subsections:

Defining time granularity
Finding the right time granularity
Aggregating time series data

Defining time granularity

Time granularity refers to the time interval between the observations within a time series. For example, if we record a financial KPI at the end of each year, then the granularity of the time series is yearly. If a glucose monitor reports the glucose level every minute, then the granularity of the time series is by the minute. In general, time granularity can be any time interval: daily, weekly, monthly, quarterly, and more.

To illustrate how time granularity determines the dynamics of a time series, the following screenshot...

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

Table of Contents (20) Chapters

Time granularity and time aggregation

Defining time granularity

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Authors (4)

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