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.

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Spis treści

Codeless Time Series Analysis with KNIME. A practical guide to implementing forecasting models for time series analysis applications eBook -- spis treści

• 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 H2O to Predict Stock Prices