Codeless Time Series Analysis with KNIME

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 and H2O with KNIME Analytics Platform within the same application

Book Description

This book will take you on a practical journey, teaching you how to implement solutions for many use cases involving time series analysis techniques.

The 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 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. In a time series analysis book, a solution for the stock price prediction couldn't be missing. This use case is covered at the end of the book together with a few more demand prediction use cases, relying on the integration of KNIME Analytics Platform and other external tools.

By the end of this time series book, you'll have learned all about time series analysis techniques and algorithms, the KNIME Analytics Platform and its time series extension, and how to apply both to common use cases.

What you will learn

• Install and configure the 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 feedforward neural networks (FFNN) and long short-term memory (LSTM) neural networks for predictive analysis
• Implement multivariate analysis by enabling GPU training for neural networks

Who This 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 KNIME is assumed.

Table of Contents

1. An Introduction to Time Series Analysis
2. An 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. Predicting tomorrow's temperature using ARIMA and its variations
8. Audio Signal classification with FFT and random forest
9. Training and deploying a neural network to predict glucose level
10. An LSTM based model to predict energy demand
11. Anomaly detection: predicting failure with no failure examples
12. Predicting taxi demand on a Spark platform
13. GPU accelerated model for multivariate demand prediction
14. Combining KNIME and H2O to Predict Stock Prices