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Codeless Time Series Analysis with KNIME

Codeless Time Series Analysis with KNIME

By : KNIME AG , Corey Weisinger, Maarit Widmann, Daniele Tonini
4.8 (10)
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Codeless Time Series Analysis with KNIME

Codeless Time Series Analysis with KNIME

4.8 (10)
By: KNIME AG , Corey Weisinger, Maarit Widmann, Daniele Tonini

Overview of this book

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.
Table of Contents (20 chapters)
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Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Share Your Thoughts
1
Part 1: Time Series Basics and KNIME Analytics Platform
Part 1: Time Series Basics and KNIME Analytics Platform
Free Chapter
2
Chapter 1: Introducing Time Series Analysis
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Chapter 1: Introducing Time Series Analysis
Understanding TSA
Exploring time series properties and examples
TSA goals and applications
Exploring time series forecasting techniques
Summary
Questions
3
Chapter 2: Introduction to KNIME Analytics Platform
Chapter 2: Introduction to KNIME Analytics Platform
Exploring the KNIME software
Introducing nodes and workflows
Building your first workflow
Configuring the time series integration
Summary
Questions
4
Chapter 3: Preparing Data for Time Series Analysis
Chapter 3: Preparing Data for Time Series Analysis
Introducing different sources of time series data
Time granularity and time aggregation
Equal spacing and time alignment
Missing value imputation
Summary
Questions
5
Chapter 4: Time Series Visualization
Chapter 4: Time Series Visualization
Technical requirements
Introducing an energy consumption time series
Introducing line plots
Introducing lag plots
Introducing seasonal plots
Introducing box plots
Summary
Questions
6
Chapter 5: Time Series Components and Statistical Properties
Chapter 5: Time Series Components and Statistical Properties
Technical requirements
Trend and seasonality components
Autocorrelation
Stationarity
Summary
Questions
7
Part 2: Building and Deploying a Forecasting Model
Part 2: Building and Deploying a Forecasting Model
8
Chapter 6: Humidity Forecasting with Classical Methods
Chapter 6: Humidity Forecasting with Classical Methods
Technical requirements
The importance of predicting the weather
Streaming humidity data from an Arduino sensor
Resampling and granularity
Training and deployment
Summary
Questions
9
Chapter 7: Forecasting the Temperature with ARIMA and SARIMA Models
Chapter 7: Forecasting the Temperature with ARIMA and SARIMA Models
Recapping regression
Introducing the (S)ARIMA models
Fitting the model and generating forecasts
Summary
Further reading
Questions
10
Chapter 8: Audio Signal Classification with an FFT and a Gradient-Boosted Forest
Chapter 8: Audio Signal Classification with an FFT and a Gradient-Boosted Forest
Technical requirements
Why do we want to classify a signal?
Windowing your data
What is a transform?
The Fourier transform
Preparing data for modeling
Training a Gradient Boosted Forest
Deploying a Gradient Boosted Forest
Summary
Questions
11
Chapter 9: Training and Deploying a Neural Network to Predict Glucose Levels
Chapter 9: Training and Deploying a Neural Network to Predict Glucose Levels
Technical requirements
Glucose prediction and the glucose dataset
A quick introduction to neural networks
Training a feedforward neural network to predict glucose levels
Deploying an FFNN-based alarm system
Summary
Questions
12
Chapter 10: Predicting Energy Demand with an LSTM Model
Chapter 10: Predicting Energy Demand with an LSTM Model
Technical requirements
Introducing recurrent neural networks and LSTMs
Encoding and tensors
Training an LSTM-based neural network
Summary
13
Chapter 11: Anomaly Detection – Predicting Failure with No Failure Examples
Chapter 11: Anomaly Detection – Predicting Failure with No Failure Examples
Technical requirements
Introducing the problem of anomaly detection in predictive maintenance
Detecting anomalies with a control chart
Predicting the next sample in a correctly working system with an auto-regressive model
Summary
Questions
14
Part 3: Forecasting on Mixed Platforms
Part 3: Forecasting on Mixed Platforms
15
Chapter 12: Predicting Taxi Demand on the Spark Platform
Chapter 12: Predicting Taxi Demand on the Spark Platform
Technical requirements
Predicting taxi demand in NYC
Connecting to the Spark platform and preparing the data
Training a random forest model on Spark
Building the deployment application
Summary
Questions
16
Chapter 13: GPU Accelerated Model for Multivariate Forecasting
Chapter 13: GPU Accelerated Model for Multivariate Forecasting
Technical requirements
From univariate to multivariate – extending the prediction problem
Building and training the multivariate neural architecture
Enabling GPU execution for neural networks
Building the deployment application
Summary
Questions
17
Chapter 14: Combining KNIME and H2O to Predict Stock Prices
Chapter 14: Combining KNIME and H2O to Predict Stock Prices
Technical requirements
Introducing the stock price prediction problem
Describing the KNIME H2O Machine Learning Integration
Accessing and preparing data within KNIME
Training an H2O model from within KNIME
Consuming the H2O model in the deployment application
Summary
Questions
Final note
18
Answers
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Chapter 12
Chapter 13
Chapter 14
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Understanding TSA

When analyzing business data, it’s quite common to focus on what happened at a particular point in time: sales figures at the end of the month, customer characteristics at the end of the year, conversion results at the end of a marketing campaign, and more. Even in the development of the most sophisticated ML models, in most cases, we collect information that refers to different objects at a specific instant in time (or by taking a few snapshots of historical data). This approach, which is absolutely valid and correct for many applications, not only in business, uses cross-sectional data as the basis for analytics: data collected by observing many subjects (such as individuals, companies, shops, countries, equipment, and more) at one point or period of time.

Although the fact of not considering the temporal factor in the analysis is widespread and rooted in common practice, there are several situations where the analysis of the temporal evolution of a phenomenon provides more complete and interesting results. In fact, it’s only through the analysis of the temporal dynamics of the data that it is possible to identify the presence of some peculiar characteristics of the phenomenon we are analyzing, be it sales/consumption data, rather than a physical parameter or a macroeconomic index. These characteristics that act over time, such as trends, periodic fluctuations, level changes, anomalous observations, turning points, and more can have an effect in the short or long term, and often, it is important to be able to measure them precisely. Furthermore, it is only by analyzing data over time that it is possible to provide a reliable quantitative estimate of what might occur in the future (whether immediate or not). Since economic conditions are constantly changing over time, data analysts must be able to assess and predict the effects of these changes in order to suggest the most appropriate actions to take for the future.

For these reasons, TSA can be a very useful tool in the hands of business analysts and data scientists when it comes to both describing the patterns of a phenomenon along the time axis and providing a reliable forecast for it. Through the use of the right tools, TSA can significantly expand the understanding of any variable of interest (typically numerical) such as sales, financial KPIs, logistic metrics, sensors’ measurements, and more. More accurate and less biased forecasts that have been obtained through quantitative TSA can be one of the most effective drivers of performance in many fields and industries.

In the next sections of this chapter, we will provide definitions, examples, and some additional elements to gain a further understanding of how to recognize some key features of time series and how to approach their analyses in a structured way.

End of Section 2
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