Codeless Time Series Analysis with KNIME: A practical guide to implementing forecasting models for time series analysis applications
Author: Corey Weisinger，Maarit Widmann，Daniele Tonini(Author)
Publisher finelybook 出版社: Packt Publishing (August 19, 2022)
Language 语言: English
Print Length 页数: 392 pages
ISBN-10: 1803232064
ISBN-13: 9781803232065
Book Description
Perform time series analysis using KNIME Analytics Platform, covering both statistical methods and machine learning-based methods
Key Features
Gain a solid understanding of time series analysis and its applications using KNIME
Learn how to apply popular statistical and machine learning time series analysis techniques
Integrate other tools such as Spark, H2O, and Keras with KNIME within the same application
Book Description
This book will take you on a practical journey, teaching you how to implement solutions for many use cases involving time series analysis techniques.
This learning journey is organized in a crescendo of difficulty, starting from the easiest yet effective techniques applied to weather forecasting, then introducing ARIMA and its variations, moving on to machine learning for audio signal classification, training deep learning architectures to predict glucose levels and electrical energy demand, and ending with an approach to anomaly detection in IoT. There’s no time series analysis book without a solution for stock price predictions and you’ll find this use case at the end of the book, together with a few more demand prediction use cases that rely on the integration of KNIME Analytics Platform and other external tools.
Author: the end of this time series book, you’ll have learned about popular time series analysis techniques and algorithms, KNIME Analytics Platform, its time series extension, and how to apply both to common use cases.
What you will learn
Install and configure KNIME time series integration
Implement common preprocessing techniques before analyzing data
Visualize and display time series data in the form of plots and graphs
Separate time series data into trends, seasonality, and residuals
Train and deploy FFNN and LSTM to perform predictive analysis
Use multivariate analysis Author: enabling GPU training for neural networks
Train and deploy an ML-based forecasting model using Spark and H2O
Who this book is for
This book is for data analysts and data scientists who want to develop forecasting applications on time series data. While no coding skills are required thanks to the codeless implementation of the examples, basic knowledge of KNIME Analytics Platform is assumed. The first part of the book targets beginners in time series analysis, and the subsequent parts of the book challenge both beginners as well as advanced users Author: introducing real-world time series applications.
Table of Contents
1. Introducing Time Series Analysis
2. Introduction to KNIME Analytics Platform
3. Preparing Data for Time Series Analysis
4. Time Series Visualization
5. Time Series Components and Statistical Properties
6. Humidity Forecasting with Classical Methods
7. Forecasting the Temperature with ARIMA and SARIMA Models
8. Audio Signal Classification with an FFT and a Gradient Boosted Forest
9. Training and Deploying a Neural Network to Predict Glucose Levels
10. Predicting Energy Demand with an LSTM Model
11. Anomaly Detection – Predicting Failure with No Failure Examples
12. Predicting Taxi Demand on the Spark Platform
13. GPU Accelerated Model for Multivariate Forecasting
14. Combining KNIME and H20 to Predict Stock Prices
