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Codeless Time Series Analysis with KNIME

Author: Corey Weisinger

Publisher: Packt Publishing Ltd

Published: 2022-08-19

Total Pages: 392

ISBN-13: 1803239972

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.

Codeless Deep Learning with KNIME

Author: Kathrin Melcher

Publisher: Packt Publishing Ltd

Published: 2020-11-27

Total Pages: 385

ISBN-13: 180056242X

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Discover how to integrate KNIME Analytics Platform with deep learning libraries to implement artificial intelligence solutions Key FeaturesBecome well-versed with KNIME Analytics Platform to perform codeless deep learningDesign and build deep learning workflows quickly and more easily using the KNIME GUIDiscover different deployment options without using a single line of code with KNIME Analytics PlatformBook Description KNIME Analytics Platform is an open source software used to create and design data science workflows. This book is a comprehensive guide to the KNIME GUI and KNIME deep learning integration, helping you build neural network models without writing any code. It’ll guide you in building simple and complex neural networks through practical and creative solutions for solving real-world data problems. Starting with an introduction to KNIME Analytics Platform, you’ll get an overview of simple feed-forward networks for solving simple classification problems on relatively small datasets. You’ll then move on to build, train, test, and deploy more complex networks, such as autoencoders, recurrent neural networks (RNNs), long short-term memory (LSTM), and convolutional neural networks (CNNs). In each chapter, depending on the network and use case, you’ll learn how to prepare data, encode incoming data, and apply best practices. By the end of this book, you’ll have learned how to design a variety of different neural architectures and will be able to train, test, and deploy the final network. What you will learnUse various common nodes to transform your data into the right structure suitable for training a neural networkUnderstand neural network techniques such as loss functions, backpropagation, and hyperparametersPrepare and encode data appropriately to feed it into the networkBuild and train a classic feedforward networkDevelop and optimize an autoencoder network for outlier detectionImplement deep learning networks such as CNNs, RNNs, and LSTM with the help of practical examplesDeploy a trained deep learning network on real-world dataWho this book is for This book is for data analysts, data scientists, and deep learning developers who are not well-versed in Python but want to learn how to use KNIME GUI to build, train, test, and deploy neural networks with different architectures. The practical implementations shown in the book do not require coding or any knowledge of dedicated scripts, so you can easily implement your knowledge into practical applications. No prior experience of using KNIME is required to get started with this book.

Data Literacy in Practice

Author: Angelika Klidas

Publisher: Packt Publishing Ltd

Published: 2022-11-30

Total Pages: 396

ISBN-13: 1803232358

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Accelerate your journey to smarter decision making by mastering the fundamentals of data literacy and developing the mindset to work confidently with data Key FeaturesGet a solid grasp of data literacy fundamentals to support your next steps in your careerLearn how to work with data and extract meaningful insights to take the right actionsApply your knowledge to real-world business intelligence projectsBook Description Data is more than a mere commodity in our digital world. It is the ebb and flow of our modern existence. Individuals, teams, and enterprises working with data can unlock a new realm of possibilities. And the resultant agility, growth, and inevitable success have one origin—data literacy. This comprehensive guide is written by two data literacy pioneers, each with a thorough footprint within the data and analytics commercial world and lectures at top universities in the US and the Netherlands. Complete with best practices, practical models, and real-world examples, Data Literacy in Practice will help you start making your data work for you by building your understanding of data literacy basics and accelerating your journey to independently uncovering insights. You'll learn the four-pillar model that underpins all data and analytics and explore concepts such as measuring data quality, setting up a pragmatic data management environment, choosing the right graphs for your readers, and questioning your insights. By the end of the book, you'll be equipped with a combination of skills and mindset as well as with tools and frameworks that will allow you to find insights and meaning within your data for data-informed decision making. What you will learnStart your data literacy journey with simple and actionable stepsApply the four-pillar model for organizations to transform data into insightsDiscover which skills you need to work confidently with dataVisualize data and create compelling visual data storiesMeasure, improve, and leverage your data to meet organizational goalsMaster the process of drawing insights, ask critical questions and action your insightsDiscover the right steps to take when you analyze insightsWho this book is for This book is for data analysts, data professionals, and data teams starting or wanting to accelerate their data literacy journey. If you're looking to develop the skills and mindset you need to work independently with data, as well as a solid knowledge base of the tools and frameworks, you'll find this book useful.

Practical Time Series Analysis

Author: Dr. Avishek Pal

Publisher: Packt Publishing Ltd

Published: 2017-09-28

Total Pages: 238

ISBN-13: 178829419X

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Step by Step guide filled with real world practical examples. About This Book Get your first experience with data analysis with one of the most powerful types of analysis—time-series. Find patterns in your data and predict the future pattern based on historical data. Learn the statistics, theory, and implementation of Time-series methods using this example-rich guide Who This Book Is For This book is for anyone who wants to analyze data over time and/or frequency. A statistical background is necessary to quickly learn the analysis methods. What You Will Learn Understand the basic concepts of Time Series Analysis and appreciate its importance for the success of a data science project Develop an understanding of loading, exploring, and visualizing time-series data Explore auto-correlation and gain knowledge of statistical techniques to deal with non-stationarity time series Take advantage of exponential smoothing to tackle noise in time series data Learn how to use auto-regressive models to make predictions using time-series data Build predictive models on time series using techniques based on auto-regressive moving averages Discover recent advancements in deep learning to build accurate forecasting models for time series Gain familiarity with the basics of Python as a powerful yet simple to write programming language In Detail Time Series Analysis allows us to analyze data which is generated over a period of time and has sequential interdependencies between the observations. This book describes special mathematical tricks and techniques which are geared towards exploring the internal structures of time series data and generating powerful descriptive and predictive insights. Also, the book is full of real-life examples of time series and their analyses using cutting-edge solutions developed in Python. The book starts with descriptive analysis to create insightful visualizations of internal structures such as trend, seasonality and autocorrelation. Next, the statistical methods of dealing with autocorrelation and non-stationary time series are described. This is followed by exponential smoothing to produce meaningful insights from noisy time series data. At this point, we shift focus towards predictive analysis and introduce autoregressive models such as ARMA and ARIMA for time series forecasting. Later, powerful deep learning methods are presented, to develop accurate forecasting models for complex time series, and under the availability of little domain knowledge. All the topics are illustrated with real-life problem scenarios and their solutions by best-practice implementations in Python. The book concludes with the Appendix, with a brief discussion of programming and solving data science problems using Python. Style and approach This book takes the readers from the basic to advance level of Time series analysis in a very practical and real world use cases.

Advances in Cybernetics, Cognition, and Machine Learning for Communication Technologies

Author: Vinit Kumar Gunjan

Publisher: Springer

Published: 2021-04-29

Total Pages: 0

ISBN-13: 9789811531279

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This book highlights recent advances in Cybernetics, Machine Learning and Cognitive Science applied to Communications Engineering and Technologies, and presents high-quality research conducted by experts in this area. It provides a valuable reference guide for students, researchers and industry practitioners who want to keep abreast of the latest developments in this dynamic, exciting and interesting research field of communication engineering, driven by next-generation IT-enabled techniques. The book will also benefit practitioners whose work involves the development of communication systems using advanced cybernetics, data processing, swarm intelligence and cyber-physical systems; applied mathematicians; and developers of embedded and real-time systems. Moreover, it shares insights into applying concepts from Machine Learning, Cognitive Science, Cybernetics and other areas of artificial intelligence to wireless and mobile systems, control systems and biomedical engineering.

Time Series Analysis

Author: Jonathan D. Cryer

Publisher: Springer Science & Business Media

Published: 2008-04-04

Total Pages: 501

ISBN-13: 0387759581

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This book presents an accessible approach to understanding time series models and their applications. The ideas and methods are illustrated with both real and simulated data sets. A unique feature of this edition is its integration with the R computing environment.

Hands-On Time Series Analysis with R

Author: Rami Krispin

Publisher: Packt Publishing Ltd

Published: 2019-05-31

Total Pages: 438

ISBN-13: 1788624041

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Build efficient forecasting models using traditional time series models and machine learning algorithms. Key FeaturesPerform time series analysis and forecasting using R packages such as Forecast and h2oDevelop models and find patterns to create visualizations using the TSstudio and plotly packagesMaster statistics and implement time-series methods using examples mentionedBook Description Time series analysis is the art of extracting meaningful insights from, and revealing patterns in, time series data using statistical and data visualization approaches. These insights and patterns can then be utilized to explore past events and forecast future values in the series. This book explores the basics of time series analysis with R and lays the foundations you need to build forecasting models. You will learn how to preprocess raw time series data and clean and manipulate data with packages such as stats, lubridate, xts, and zoo. You will analyze data and extract meaningful information from it using both descriptive statistics and rich data visualization tools in R such as the TSstudio, plotly, and ggplot2 packages. The later section of the book delves into traditional forecasting models such as time series linear regression, exponential smoothing (Holt, Holt-Winter, and more) and Auto-Regressive Integrated Moving Average (ARIMA) models with the stats and forecast packages. You'll also cover advanced time series regression models with machine learning algorithms such as Random Forest and Gradient Boosting Machine using the h2o package. By the end of this book, you will have the skills needed to explore your data, identify patterns, and build a forecasting model using various traditional and machine learning methods. What you will learnVisualize time series data and derive better insightsExplore auto-correlation and master statistical techniquesUse time series analysis tools from the stats, TSstudio, and forecast packagesExplore and identify seasonal and correlation patternsWork with different time series formats in RExplore time series models such as ARIMA, Holt-Winters, and moreEvaluate high-performance forecasting solutionsWho this book is for Hands-On Time Series Analysis with R is ideal for data analysts, data scientists, and all R developers who are looking to perform time series analysis to predict outcomes effectively. A basic knowledge of statistics is required; some knowledge in R is expected, but not mandatory.

Time Series Analysis

Author: Chun-Kit Ngan

Publisher:

Published: 2019

Total Pages: 130

ISBN-13: 9781789847864

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This book aims to provide readers with the current information, developments, and trends in a time series analysis, particularly in time series data patterns, technical methodologies, and real-world applications. This book is divided into three sections and each section includes two chapters. Section 1 discusses analyzing multivariate and fuzzy time series. Section 2 focuses on developing deep neural networks for time series forecasting and classification. Section 3 describes solving real-world domain-specific problems using time series techniques. The concepts and techniques contained in this book cover topics in time series research that will be of interest to students, researchers, practitioners, and professors in time series forecasting and classification, data analytics, machine learning, deep learning, and artificial intelligence.

Mastering Machine Learning with R

Author: Cory Lesmeister

Publisher: Packt Publishing Ltd

Published: 2015-10-28

Total Pages: 400

ISBN-13: 1783984538

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Master machine learning techniques with R to deliver insights for complex projects About This Book Get to grips with the application of Machine Learning methods using an extensive set of R packages Understand the benefits and potential pitfalls of using machine learning methods Implement the numerous powerful features offered by R with this comprehensive guide to building an independent R-based ML system Who This Book Is For If you want to learn how to use R's machine learning capabilities to solve complex business problems, then this book is for you. Some experience with R and a working knowledge of basic statistical or machine learning will prove helpful. What You Will Learn Gain deep insights to learn the applications of machine learning tools to the industry Manipulate data in R efficiently to prepare it for analysis Master the skill of recognizing techniques for effective visualization of data Understand why and how to create test and training data sets for analysis Familiarize yourself with fundamental learning methods such as linear and logistic regression Comprehend advanced learning methods such as support vector machines Realize why and how to apply unsupervised learning methods In Detail Machine learning is a field of Artificial Intelligence to build systems that learn from data. Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning to your data. The book starts with introduction to Cross-Industry Standard Process for Data Mining. It takes you through Multivariate Regression in detail. Moving on, you will also address Classification and Regression trees. You will learn a couple of “Unsupervised techniques”. Finally, the book will walk you through text analysis and time series. The book will deliver practical and real-world solutions to problems and variety of tasks such as complex recommendation systems. By the end of this book, you will gain expertise in performing R machine learning and will be able to build complex ML projects using R and its packages. Style and approach This is a book explains complicated concepts with easy to follow theory and real-world, practical applications. It demonstrates the power of R and machine learning extensively while highlighting the constraints.

Learning Quantitative Finance with R

Author: Dr. Param Jeet

Publisher: Packt Publishing Ltd

Published: 2017-03-23

Total Pages: 276

ISBN-13: 1786465256

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Implement machine learning, time-series analysis, algorithmic trading and more About This Book Understand the basics of R and how they can be applied in various Quantitative Finance scenarios Learn various algorithmic trading techniques and ways to optimize them using the tools available in R. Contain different methods to manage risk and explore trading using Machine Learning. Who This Book Is For If you want to learn how to use R to build quantitative finance models with ease, this book is for you. Analysts who want to learn R to solve their quantitative finance problems will also find this book useful. Some understanding of the basic financial concepts will be useful, though prior knowledge of R is not required. What You Will Learn Get to know the basics of R and how to use it in the field of Quantitative Finance Understand data processing and model building using R Explore different types of analytical techniques such as statistical analysis, time-series analysis, predictive modeling, and econometric analysis Build and analyze quantitative finance models using real-world examples How real-life examples should be used to develop strategies Performance metrics to look into before deciding upon any model Deep dive into the vast world of machine-learning based trading Get to grips with algorithmic trading and different ways of optimizing it Learn about controlling risk parameters of financial instruments In Detail The role of a quantitative analyst is very challenging, yet lucrative, so there is a lot of competition for the role in top-tier organizations and investment banks. This book is your go-to resource if you want to equip yourself with the skills required to tackle any real-world problem in quantitative finance using the popular R programming language. You'll start by getting an understanding of the basics of R and its relevance in the field of quantitative finance. Once you've built this foundation, we'll dive into the practicalities of building financial models in R. This will help you have a fair understanding of the topics as well as their implementation, as the authors have presented some use cases along with examples that are easy to understand and correlate. We'll also look at risk management and optimization techniques for algorithmic trading. Finally, the book will explain some advanced concepts, such as trading using machine learning, optimizations, exotic options, and hedging. By the end of this book, you will have a firm grasp of the techniques required to implement basic quantitative finance models in R. Style and approach This book introduces you to the essentials of quantitative finance with the help of easy-to-understand, practical examples and use cases in R. Each chapter presents a specific financial concept in detail, backed with relevant theory and the implementation of a real-life example.