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, H2O, and Keras with KNIME 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.

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.

What you will learn

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

Who this book is for

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.

Table of Contents

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