Codeless Time Series Analysis with KNIME: A practical guide to implementing forecasting models for time series analysis applications
Category
Publication
Packt Publishing
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.
By 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.
Review
"Codeless Time Series Analysis with KNIME is a perfect storm of codeless data science meets time series analysis meets one of the most popular analytics platforms available. If you want to learn not just about time series forecasting but how to implement practical applications of it, Corey Weisinger, Maarit Widmann, and Daniele Tonini have written the guide you need. Youll learn about common time series data preprocessing. This step is important to any sort of analytics, but for time series forecasting it is absolutely vital, and not understanding some of the particularities can leave you stranded without hope of success. You will also learn the machine learning techniques to perform the forecasting after your data has been properly prepared and that will hopefully lead to results that will make the process worthwhile."
--Matthew Mayo, Data Scientist, Editor-in-Chief at KDnuggets
"I have been teaching analytics and data science courses, both academically and professionally, and often in those courses, I cover quite a bit of time series forecasting. This book will be an excellent resource for my students and me because it focuses on concepts and theory of time series, as opposed to syntactic details of coding with a programming language. KNIME has been my primary software tool to teach business analytics for a long time, and now, this book will make it easier for me to cover the time series forecasting aspects of the subject."
--Dr. Dursun Delen, Professor of Management Science & Information Systems at Oklahoma State University
This book deals with time series analysis: from pure concepts to implementation, from dataviz to modeling; from ARIMA to deep learning, each chapter describes a specific use case. A lot to learn from this resource.
-- Rosaria Silipo, Head of Data Science Evangelism at KNIME
"If you want to get serious with time series analysis, this book is a must! Not only will the readers become comfortable with the fundamental concepts of time series (stationarity, trend, seasonality, autoregression, frequency transforms, and so on), but they will also experience real-world applications such as audio signal classification, weather forecasting, energy demand prediction, and failure detection.
It's rare to find a book that covers the full end-to-end process of time series analysis in a codeless environment. I totally recommend reading it!"
--Andrea De Mauro, author of "Data Analytics Made Easy", Head of Data & Analytics at Vodafone
--This text refers to the paperback edition.About the Author
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 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 in the KNIME courses. She is the author of the From Modeling to Model Evaluation e-book and she publishes regularly in 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 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.
--This text refers to the paperback edition.- 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.
- 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
