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

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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

Building the deployment application

In this section, we introduce a deployment application that predicts the hourly trip counts on one day. We selected to predict the trip counts on July 1, 2018. We perform both static and dynamic deployment as introduced in the following subsections:

Predicting the trip count in the next hour (static deployment)
Predicting the trip count in the next 24 hours (dynamic deployment)

Predicting the trip count in the next hour

The goal of this subsection is to predict the demand for taxis in the first hour on July 1, 2018, so at 00:00.

The required seed data for the prediction contains the 24 past values representing the hourly trip counts on June 30, 2018, and in addition, the hour of the day (0) and the day of the week (1 since July 1, 2008, was a Sunday). We access the seed data already pre-processed as a Parquet file. The workflow containing the static deployment (accessible via https://kni.me/w/wl2B4B5LNvj0Z4K9) is shown in...

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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

Building the deployment application

Predicting the trip count in the next hour

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Authors (4)

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