Description: Codeless Time Series Analysis with KNIME by Corey Weisinger, Maarit Widmann, Daniele Tonini This book will take you through time series analysis techniques and algorithms, from both statistical and machine learning domains. It will introduce you to the KNIME Analytics Platform and its time series extension for implementing practical solutions for demand prediction, signal classification, signal forecasting, and anomaly detection. FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher Description Perform time series analysis using KNIME Analytics Platform, covering both statistical methods and machine learning-based methodsKey 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 DescriptionThis 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. Theres no time series analysis book without a solution for stock price predictions and youll 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, youll 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 forThis 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. Author Biography Corey Weisinger is a data scientist with KNIME in Austin, Texas. He studied mathematics at Michigan State University focusing on actuarial techniques and functional analysis. Before coming to work for KNIME, he worked as an analytics consultant for the auto industry in Detroit, Michigan. He currently focuses on signal processing and numeric prediction techniques and is the author of the Alteryx to KNIME guidebook.Maarit Widmann is a data scientist and an educator at KNIME: the instructor behind the KNIME self-paced courses and a teacher in the KNIME courses. She is the author of the From Modeling to Model Evaluation e-book and she publishes regularly in the KNIME blog and on Medium. She holds a Masters degree in data science and a Bachelors degree in sociology.Daniele Tonini is an experienced advisor and educator in the field of advanced business analytics and machine learning. In the last 15 years, he designed and deployed predictive analytics systems, and data quality management and dynamic reporting tools, mainly for customer intelligence, risk management, and pricing applications. He is an Academic Fellow at Bocconi University (Department of Decision Science) and SDA Bocconi School of Management (Decision Sciences & Business Analytics Faculty). Hes also Adjunct Professor in data mining at Franklin University, Switzerland. He currently teaches statistics, predictive analytics for data-driven decision making, big data and databases, market research, and data mining. Table of Contents 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 Details ISBN1803232064 Author Daniele Tonini Short Title Codeless Time Series Analysis with KNIME Pages 392 Language English Year 2022 ISBN-10 1803232064 ISBN-13 9781803232065 Format Paperback Publisher Packt Publishing Limited Imprint Packt Publishing Limited Place of Publication Birmingham Country of Publication United Kingdom Subtitle A practical guide to implementing forecasting models for time series analysis applications Publication Date 2022-03-30 AU Release Date 2022-03-30 NZ Release Date 2022-03-30 UK Release Date 2022-03-30 DEWEY 519.550285 Audience General We've got this At The Nile, if you're looking for it, we've got it. 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Format: Paperback
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Author: Corey Weisinger, Maarit Widmann, Daniele Tonini
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Book Title: Codeless Time Series Analysis with KNIME
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