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

Description

Perform time series analysis using KNIME Analytics Platform, covering both statistical methods and machine learning-based methods Key FeaturesGain a solid understanding of time series analysis and its applications using KNIMELearn how to apply popular statistical and machine learning time series analysis techniquesIntegrate other tools such as Spark, H2O, and Keras with KNIME within the same applicationBook 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 learnInstall and configure KNIME time series integrationImplement common preprocessing techniques before analyzing dataVisualize and display time series data in the form of plots and graphsSeparate time series data into trends, seasonality, and residualsTrain and deploy FFNN and LSTM to perform predictive analysisUse multivariate analysis by enabling GPU training for neural networksTrain and deploy an ML-based forecasting model using Spark and H2OWho 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 ContentsIntroducing Time Series AnalysisIntroduction to KNIME Analytics PlatformPreparing Data for Time Series AnalysisTime Series VisualizationTime Series Components and Statistical PropertiesHumidity Forecasting with Classical MethodsForecasting the Temperature with ARIMA and SARIMA ModelsAudio Signal Classification with an FFT and a Gradient Boosted ForestTraining and Deploying a Neural Network to Predict Glucose LevelsPredicting Energy Demand with an LSTM ModelAnomaly Detection – Predicting Failure with No Failure ExamplesPredicting Taxi Demand on the Spark PlatformGPU Accelerated Model for Multivariate ForecastingCombining KNIME and H2O to Predict Stock Prices Cover Title Page Copyright and Credits Dedication Contributors Table of Contents Preface Part 1: Time Series Basics and KNIME Analytics Platform Chapter 1: Introducing Time Series Analysis Understanding TSA Exploring time series properties and examples Continuous and discrete time series Independence and serial correlation Time series examples TSA goals and applications Goals of TSA Domains of applications and use cases Exploring time series forecasting techniques Quantitative forecasting properties and techniques Summary Questions Chapter 2: Introduction to KNIME Analytics Platform Exploring the KNIME software Introducing KNIME Analytics Platform for creating data science applications Introducing KNIME Server for productionizing data science applications Introducing nodes and workflows Introducing nodes Introducing workflows Searching for and sharing resources on the KNIME Hub Building your first workflow Creating a new workflow (group) Reading and transforming data Filtering rows Visualizing data Building a custom interactive view Documenting workflows Configuring the time series integration Introducing the time series components Configuring Python in KNIME Summary Questions Chapter 3: Preparing Data for Time Series Analysis Introducing different sources of time series data Time granularity and time aggregation Defining time granularity Finding the right time granularity Aggregating time series data Equal spacing and time alignment Explaining the concept of equal spacing Missing value imputation Defining the different types of missing values Introducing missing value imputation techniques Summary Questions Chapter 4: Time Series Visualization Technical requirements Introducing an energy consumption time series Describing raw energy consumption data Clustering energy consumption data Introducing line plots Displaying simple dynamics with a line plot Interpreting the dynamics of a time series based on a line plot Building a line plot in KNIME Introducing lag plots Introducing insights derived from a lag plot Building a lag plot in KNIME Introducing seasonal plots Comparing seasonal patterns in a seasonal plot Building a seasonal plot in KNIME Introducing box plots Inspecting variability of data in a box plot Building a box plot in KNIME Summary Questions Chapter 5: Time Series Components and Statistical Properties Technical requirements Trend and seasonality components Trend Seasonality Decomposition Autocorrelation Stationarity Summary Questions Part 2: Building and Deploying a Forecasting Model Chapter 6: Humidity Forecasting with Classical Methods Technical requirements The importance of predicting the weather Other IoT sensors The use case Streaming humidity data from an Arduino sensor What is an Arduino? Moving data to KNIME Storing the data to create a training set Resampling and granularity Aligning data timestamps Missing values Aggregation techniques Training and deployment Types of classic models available in KNIME Training a model in KNIME Available deployment options Building the workflow Writing model predictions to a database Summary Questions Chapter 7: Forecasting the Temperature with ARIMA and SARIMA Models Recapping regression Defining a regression Introducing the (S)ARIMA models Requirements of the (S)ARIMA model How to configure the ARIMA or SARIMA model Fitting the model and generating forecasts The data Summary Further reading Questions 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 Windowing your data in KNIME What is a transform? The Fourier transform Discrete Fourier Transform (DFT) Fast Fourier Transform (FFT) Applying the Fourier transform in KNIME Preparing data for modeling Reducing dimensionality Training a Gradient Boosted Forest Applying the Fourier transform in KNIME Applying the Gradient Boosted Trees Learner Deploying a Gradient Boosted Forest Summary Questions Chapter 9: Training and Deploying a Neural Network to Predict Glucose Levels Technical requirements Glucose prediction and the glucose dataset Glucose prediction The glucose dataset A quick introduction to neural networks Artificial neurons and artificial neural networks The backpropagation algorithm Other types of neural networks Training a feedforward neural network to predict glucose levels KNIME Deep Learning Keras Integration Building the network Training the network Scoring the network and creating the output rule Deploying an FFNN-based alarm system Summary Questions Chapter 10: Predicting Energy Demand with an LSTM Model Technical requirements Introducing recurrent neural networks and LSTMs Recapping recurrent neural networks The architecture of the LSTM unit Forget Gate Input Gate Output Gate Encoding and tensors Input data Reshaping the data Training an LSTM-based neural network The Keras Network Learner node Deploying an LSTM network for future prediction Scoring the forecasts Summary Questions Chapter 11: Anomaly Detection – Predicting Failure with No Failure Examples Technical requirements Introducing the problem of anomaly detection in predictive maintenance Introducing the anomaly detection problem IoT data preprocessing Exploring anomalies visually Detecting anomalies with a control chart Introducing a control chart Implementing a control chart Predicting the next sample in a correctly working system with an auto-regressive model Introducing an auto-regressive model Training an auto-regressive model with the linear regression algorithm Deploying an auto-regressive model Summary Questions Part 3: Forecasting on Mixed Platforms 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 Introducing the Hadoop ecosystem Accessing the data and loading it into Spark Introducing the Spark compatible nodes Training a random forest model on Spark Exploring seasonalities via line plots and auto-correlation plot Preprocessing the data Training and testing the random forest model on Spark Building the deployment application Predicting the trip count in the next hour Predicting the trip count in the next 24 hours Summary Questions 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 Setting up a new GPU Python environment Switching Python environments dynamically Building the deployment application Summary Questions 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 Starting a workflow running on the H2O platform Introducing the H2O nodes for machine learning Accessing and preparing data within KNIME Accessing stock market data from Yahoo Finance Preparing the data for modeling on H2O Training an H2O model from within KNIME Optimizing the number of predictor columns Training, applying, and testing the optimized model Consuming the H2O model in the deployment application Summary Questions Final note 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 Index About Packt Other Books You May Enjoy