Corey Weisinger - Codeless Time Series Analysis with KNIME: A practical guide to implementing forecasting models for time series analysis applications
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- Book:Codeless Time Series Analysis with KNIME: A practical guide to implementing forecasting models for time series analysis applications
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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
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. Theres no time series analysis book without a solution for stock price predictions and youll 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.
By the end of this time series book, youll 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 by enabling GPU training for neural networks
- Train and deploy an ML-based forecasting model using Spark and H2O
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 by introducing real-world time series applications.
Table of Contents- Introducing Time Series Analysis
- Introduction to KNIME Analytics Platform
- Preparing Data for Time Series Analysis
- Time Series Visualization
- Time Series Components and Statistical Properties
- Humidity Forecasting with Classical Methods
- Forecasting the Temperature with ARIMA and SARIMA Models
- Audio Signal Classification with an FFT and a Gradient Boosted Forest
- Training and Deploying a Neural Network to Predict Glucose Levels
- Predicting Energy Demand with an LSTM Model
- Anomaly Detection Predicting Failure with No Failure Examples
- Predicting Taxi Demand on the Spark Platform
- GPU Accelerated Model for Multivariate Forecasting
- Combining KNIME and H2O to Predict Stock Prices
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A practical guide to implementing forecasting models for time series analysis applications
Corey Weisinger
Maarit Widmann
Daniele Tonini
BIRMINGHAMMUMBAI
Copyright 2022 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Group Product Manager: Reshma Raman
Publishing Product Manager: Reshma Raman
Senior Editor: Nithya Sadanandan
Technical Editor: Pradeep Sahu
Copy Editor: Safis Editing
Project Coordinator: Deeksha Thakkar
Proofreader: Safis Editing
Indexer: Manju Arasan
Production Designer: Prashant Ghare
Marketing Coordinator: Priyanka Mhatre
First published: July 2022
Production reference: 1220722
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham
B3 2PB, UK.
ISBN 978-1-80323-206-5
www.packt.com
Thanks to my colleagues at KNIME for the technical support and encouragement, especially to Andisa Dewi and Tobias Ktter for the Taxi Demand Prediction application, and Rosaria Silipo, Phil Winters, and Iris Ad for the Anomaly Detection application.
Maarit Widmann
I would like to thank the KNIME team for including me in this great project. Especially thanks to Rosaria Silipo, for her trust and support, and to my co-authors Maarit and Corey, for taking this long journey with me.
Daniele Tonini
Corey Weisinger is a data scientist with KNIME in Austin, Texas. He studied mathematics at Michigan State University focusing on actuarial techniques and functional analysis. Before coming to work for KNIME, he worked as an analytics consultant for the auto industry in Detroit, Michigan. He currently focuses on signal processing and numeric prediction techniques and is the author of the From Alteryx to KNIME guidebook.
Maarit Widmann is a data scientist and an educator at KNIME: the instructor behind the KNIME self-paced courses and a teacher of the KNIME courses. She is the author of the From Modeling to Model Evaluation eBook and she publishes regularly on the KNIME blog and on Medium. She holds a masters degree in data science and a bachelors degree in sociology.
Daniele Tonini is an experienced advisor and educator in the field of advanced business analytics and machine learning. In the last 15 years, he designed and deployed predictive analytics systems, and data quality management and dynamic reporting tools, mainly for customer intelligence, risk management, and pricing applications. He is an Academic Fellow at Bocconi University (Department of Decision Science) and SDA Bocconi School of Management (Decision Sciences & Business Analytics Faculty). Hes also an adjunct professor in data mining at Franklin University, Switzerland. He currently teaches statistics, predictive analytics for data-driven decision making, big data and databases, market research, and data mining.
Miguel Infestas Maderuelo has a Ph.D. in applied economics and has developed his career around data analytics in different fields (digital marketing, data mining, academic research, and so on). His last project is as a founder of a digital marketing agency, applying analytics on digital data to optimize digital communication.
Rosaria Silipo, Ph.D., now head of data science evangelism at KNIME, has spent 25+ years in applied AI, predictive analytics, and machine learning at Siemens, Viseca, Nuance Communications, and private consulting. Sharing her practical experience in a broad range of industries and deployments, including IoT, customer intelligence, financial services, social media, and cybersecurity, Rosaria has authored 50+ technical publications, including her recent books Guide to Intelligent Data Science (Springer) and Codeless Deep Learning with KNIME (Packt).
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