Who the book is for
This book is for data analysts and data scientists who want to develop forecasting applications on time series data. Basic knowledge of data transformations is assumed, while no coding skills are required thanks to the codeless implementation of the examples. The first part of the book targets the beginners in time series analysis by introducing the main concepts of time series analysis and visual exploration and preprocessing of time series data. The subsequent parts of the book challenge both beginners and advanced users by introducing real-world time series analysis applications.
What you'll learn
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.