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

Which of the following does not contribute to more efficient data processing in a cluster environment?
1. Connecting to a cluster of several machines
2. Using Parquet file format
3. Executing via Spark
4. Retrieving data locall.
Performing Spark tasks in your workflows requires…
1. Data as a Spark data frame
2. A dedicated Spark node for the task
3. A remote cluster
4. Data in Parquet forma.
Which of the following often determines the appropriate granularity of the historical data?
1. The available resources for the computation
2. The forecast horizon
3. The forecasting algorithm
4. The number of predictor column.
Could you apply the same demand prediction model to forecast the trip count tomorrow?
1. Yes, if the seasonality pattern is the same and there is no trend through the years.
2. No, the model needs to be retrained as soon as enough historical data becomes available.
3. Yes, if you increase the size of the seed data.
4. No, because the model was trained on data with many outliers.

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