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

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

Questions

Increasing the size of the neural network makes the training time…
1. Shorter if the input values are correlated
2. Longer
3. Shorter
4. Longer if the input values are not correlated
How can you enable a workflow to execute on GPU?
1. Through the settings on the dedicated Preference page
2. Via the BackPropagation algorithm
3. Through normalization
4. Through the CPU settings
Why should we use the Conda Environment Propagation node for GPU execution?
1. To switch the default execution from CPU to GPU.
2. We always have to use a Conda Environment Propagation node for GPU execution.
3. If we want to execute the current workflow on a GPU while leaving default execution on the CPU.
4. To reduce the number of weights in the network.
What is a Dropout layer for?
1. To optimize the training algorithm for certain data types
2. To increase the size of the network
3. To increase the complexity of the network
4. To help regularize the training of the network

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