KNIME AG

Corey Weisinger

Maarit Widmann

Daniele Tonini

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.

Packt Publishing

Codeless Time Series Analysis with KNIME: A practical guide to implementing forecasting models for time series analysis applications

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