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Codeless Time Series Analysis with KNIME

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

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Product type Paperback
Published in Aug 2022
Publisher Packt
ISBN-13 9781803232065
Length 392 pages
Edition 1st Edition
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Authors (4):
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Daniele Tonini Daniele Tonini
Author Profile Icon Daniele Tonini
Daniele Tonini
Maarit Widmann Maarit Widmann
Author Profile Icon Maarit Widmann
Maarit Widmann
Corey Weisinger Corey Weisinger
Author Profile Icon Corey Weisinger
Corey Weisinger
KNIME AG KNIME AG
Author Profile Icon KNIME AG
KNIME AG
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Toc

Table of Contents (20) Chapters Close

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

Preface

This book gives an overview of the basics of time series data and time series analysis and of KNIME Analytics Platform and its time series integration. It shows how to implement practical solutions for a wide range of use cases, from demand prediction to signal classification and signal forecasting, and from price prediction to anomaly detection. It also demonstrates how to integrate other tools in addition to KNIME Analytics Platform within the same application.

The book instructs you on common preprocessing steps of time series data and statistics and machine learning-based techniques for forecasting. These things need to be learned to master the field of time series analysis. The book also points you to examples implemented in KNIME Analytics Platform, which is a visual programming tool that is accessible and fast to learn. This removes the common time and skill barrier of learning to code.

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