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Advanced Analytics And Learning On Temporal Data
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Book Synopsis Advanced Analytics and Learning on Temporal Data by : Georgiana Ifrim
Download or read book Advanced Analytics and Learning on Temporal Data written by Georgiana Ifrim and published by Springer Nature. This book was released on 2024-01-20 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume LNCS 14343 constitutes the refereed proceedings of the 8th ECML PKDD Workshop, AALTD 2023, in Turin, Italy, in September 2023. The 20 full papers were carefully reviewed and selected from 28 submissions. They are organized in the following topical section as follows: Machine Learning; Data Mining; Pattern Analysis; Statistics to Share their Challenges and Advances in Temporal Data Analysis.
Book Synopsis Advanced Analytics and Learning on Temporal Data by : Vincent Lemaire
Download or read book Advanced Analytics and Learning on Temporal Data written by Vincent Lemaire and published by Springer Nature. This book was released on 2020-12-15 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 4th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2019, held in Ghent, Belgium, in September 2020. The 15 full papers presented in this book were carefully reviewed and selected from 29 submissions. The selected papers are devoted to topics such as Temporal Data Clustering; Classification of Univariate and Multivariate Time Series; Early Classification of Temporal Data; Deep Learning and Learning Representations for Temporal Data; Modeling Temporal Dependencies; Advanced Forecasting and Prediction Models; Space-Temporal Statistical Analysis; Functional Data Analysis Methods; Temporal Data Streams; Interpretable Time-Series Analysis Methods; Dimensionality Reduction, Sparsity, Algorithmic Complexity and Big Data Challenge; and Bio-Informatics, Medical, Energy Consumption, Temporal Data.
Book Synopsis Advanced Analytics and Learning on Temporal Data by : Vincent Lemaire
Download or read book Advanced Analytics and Learning on Temporal Data written by Vincent Lemaire and published by Springer Nature. This book was released on 2020-01-22 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 4th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2019, held in Würzburg, Germany, in September 2019. The 7 full papers presented together with 9 poster papers were carefully reviewed and selected from 31 submissions. The papers cover topics such as temporal data clustering; classification of univariate and multivariate time series; early classification of temporal data; deep learning and learning representations for temporal data; modeling temporal dependencies; advanced forecasting and prediction models; space-temporal statistical analysis; functional data analysis methods; temporal data streams; interpretable time-series analysis methods; dimensionality reduction, sparsity, algorithmic complexity and big data challenge; and bio-informatics, medical, energy consumption, on temporal data.
Book Synopsis Advanced Analytics and Learning on Temporal Data by : Vincent Lemaire
Download or read book Advanced Analytics and Learning on Temporal Data written by Vincent Lemaire and published by Springer Nature. This book was released on 2021-12-02 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 6th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2021, held during September 13-17, 2021. The workshop was planned to take place in Bilbao, Spain, but was held virtually due to the COVID-19 pandemic. The 12 full papers presented in this book were carefully reviewed and selected from 21 submissions. They focus on the following topics: Temporal Data Clustering; Classification of Univariate and Multivariate Time Series; Multivariate Time Series Co-clustering; Efficient Event Detection; Modeling Temporal Dependencies; Advanced Forecasting and Prediction Models; Cluster-based Forecasting; Explanation Methods for Time Series Classification; Multimodal Meta-Learning for Time Series Regression; and Multivariate Time Series Anomaly Detection.
Book Synopsis Advanced Analytics and Learning on Temporal Data by : Thomas Guyet
Download or read book Advanced Analytics and Learning on Temporal Data written by Thomas Guyet and published by Springer Nature. This book was released on 2023-03-20 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 7th ECML PKDD Workshop, AALTD 2022, held in Grenoble, France, during September 19–23, 2022. The 12 full papers included in this book were carefully reviewed and selected from 21 submissions. They were organized in topical sections as follows: Oral presentation and poster presentation.
Book Synopsis Advanced Analysis and Learning on Temporal Data by : Ahlame Douzal-Chouakria
Download or read book Advanced Analysis and Learning on Temporal Data written by Ahlame Douzal-Chouakria and published by Springer. This book was released on 2016-08-03 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the First ECML PKDD Workshop, AALTD 2015, held in Porto, Portugal, in September 2016. The 11 full papers presented were carefully reviewed and selected from 22 submissions. The first part focuses on learning new representations and embeddings for time series classification, clustering or for dimensionality reduction. The second part presents approaches on classification and clustering with challenging applications on medicine or earth observation data. These works show different ways to consider temporal dependency in clustering or classification processes. The last part of the book is dedicated to metric learning and time series comparison, it addresses the problem of speeding-up the dynamic time warping or dealing with multi-modal and multi-scale metric learning for time series classification and clustering.
Book Synopsis Advanced Analytics with Spark by : Sandy Ryza
Download or read book Advanced Analytics with Spark written by Sandy Ryza and published by "O'Reilly Media, Inc.". This book was released on 2017-06-12 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the second edition of this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. Updated for Spark 2.1, this edition acts as an introduction to these techniques and other best practices in Spark programming. You’ll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques—including classification, clustering, collaborative filtering, and anomaly detection—to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you’ll find the book’s patterns useful for working on your own data applications. With this book, you will: Familiarize yourself with the Spark programming model Become comfortable within the Spark ecosystem Learn general approaches in data science Examine complete implementations that analyze large public data sets Discover which machine learning tools make sense for particular problems Acquire code that can be adapted to many uses
Book Synopsis Advanced Analytics with Spark by : Sandy Ryza
Download or read book Advanced Analytics with Spark written by Sandy Ryza and published by "O'Reilly Media, Inc.". This book was released on 2015-04-02 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. You’ll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques—classification, collaborative filtering, and anomaly detection among others—to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you’ll find these patterns useful for working on your own data applications. Patterns include: Recommending music and the Audioscrobbler data set Predicting forest cover with decision trees Anomaly detection in network traffic with K-means clustering Understanding Wikipedia with Latent Semantic Analysis Analyzing co-occurrence networks with GraphX Geospatial and temporal data analysis on the New York City Taxi Trips data Estimating financial risk through Monte Carlo simulation Analyzing genomics data and the BDG project Analyzing neuroimaging data with PySpark and Thunder
Book Synopsis Advanced Data Analytics Using Python by : Sayan Mukhopadhyay
Download or read book Advanced Data Analytics Using Python written by Sayan Mukhopadhyay and published by Apress. This book was released on 2018-03-29 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. You’ll also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP. Advanced Data Analytics Using Python also covers important traditional data analysis techniques such as time series and principal component analysis. After reading this book you will have experience of every technical aspect of an analytics project. You’ll get to know the concepts using Python code, giving you samples to use in your own projects. What You Will Learn Work with data analysis techniques such as classification, clustering, regression, and forecasting Handle structured and unstructured data, ETL techniques, and different kinds of databases such as Neo4j, Elasticsearch, MongoDB, and MySQL Examine the different big data frameworks, including Hadoop and Spark Discover advanced machine learning concepts such as semi-supervised learning, deep learning, and NLP Who This Book Is For Data scientists and software developers interested in the field of data analytics.
Download or read book R for Everyone written by Jared P. Lander and published by Addison-Wesley Professional. This book was released on 2017-06-13 with total page 1454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone, Second Edition, is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks. Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You’ll download and install R; navigate and use the R environment; master basic program control, data import, manipulation, and visualization; and walk through several essential tests. Then, building on this foundation, you’ll construct several complete models, both linear and nonlinear, and use some data mining techniques. After all this you’ll make your code reproducible with LaTeX, RMarkdown, and Shiny. By the time you’re done, you won’t just know how to write R programs, you’ll be ready to tackle the statistical problems you care about most. Coverage includes Explore R, RStudio, and R packages Use R for math: variable types, vectors, calling functions, and more Exploit data structures, including data.frames, matrices, and lists Read many different types of data Create attractive, intuitive statistical graphics Write user-defined functions Control program flow with if, ifelse, and complex checks Improve program efficiency with group manipulations Combine and reshape multiple datasets Manipulate strings using R’s facilities and regular expressions Create normal, binomial, and Poisson probability distributions Build linear, generalized linear, and nonlinear models Program basic statistics: mean, standard deviation, and t-tests Train machine learning models Assess the quality of models and variable selection Prevent overfitting and perform variable selection, using the Elastic Net and Bayesian methods Analyze univariate and multivariate time series data Group data via K-means and hierarchical clustering Prepare reports, slideshows, and web pages with knitr Display interactive data with RMarkdown and htmlwidgets Implement dashboards with Shiny Build reusable R packages with devtools and Rcpp Register your product at informit.com/register for convenient access to downloads, updates, and corrections as they become available.
Book Synopsis Advanced Big Data Analytics Professional Level by : CPA John Kimani
Download or read book Advanced Big Data Analytics Professional Level written by CPA John Kimani and published by Finstock Evarsity Publishers. This book was released on 2023-08-28 with total page 77 pages. Available in PDF, EPUB and Kindle. Book excerpt: BOOK SUMMARY The main topics in this book are; • Machine Learning Algorithms for Predictive Analysis • Natural Language Processing in Text Analytics • Graph Analytics and Network Analysis • Time Series Analysis and Forecasting • Deep Learning in Image and Video Analytics • Streaming Data Analytics • Spatial Data Analysis and Geospatial Analytics • Big Data Ethics and Privacy Considerations “Advanced Big Data Analytics” offers a comprehensive exploration of cutting-edge techniques and methodologies in the realm of big data analysis. Through a blend of theoretical insights, practical examples and real-world case studies, the book guides readers in harnessing the power of vast datasets to uncover valuable insights, make informed decisions and address contemporary data-driven challenges.
Book Synopsis Big Data Application in Power Systems by : Reza Arghandeh
Download or read book Big Data Application in Power Systems written by Reza Arghandeh and published by Elsevier. This book was released on 2017-11-27 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Application in Power Systems brings together experts from academia, industry and regulatory agencies who share their understanding and discuss the big data analytics applications for power systems diagnostics, operation and control. Recent developments in monitoring systems and sensor networks dramatically increase the variety, volume and velocity of measurement data in electricity transmission and distribution level. The book focuses on rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches to process high dimensional, heterogeneous and spatiotemporal data. The book chapters discuss challenges, opportunities, success stories and pathways for utilizing big data value in smart grids. Provides expert analysis of the latest developments by global authorities Contains detailed references for further reading and extended research Provides additional cross-disciplinary lessons learned from broad disciplines such as statistics, computer science and bioinformatics Focuses on rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches to process high dimensional, heterogeneous and spatiotemporal data
Book Synopsis Applied Time Series Analysis and Forecasting with Python by : Changquan Huang
Download or read book Applied Time Series Analysis and Forecasting with Python written by Changquan Huang and published by Springer Nature. This book was released on 2022-10-19 with total page 377 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov switching models for (non)stationary, multivariate and financial time series, but also modern machine learning procedures and challenges for time series forecasting. Providing an organic combination of the principles of time series analysis and Python programming, it enables the reader to study methods and techniques and practice writing and running Python code at the same time. Its data-driven approach to analyzing and modeling time series data helps new learners to visualize and interpret both the raw data and its computed results. Primarily intended for students of statistics, economics and data science with an undergraduate knowledge of probability and statistics, the book will equally appeal to industry professionals in the fields of artificial intelligence and data science, and anyone interested in using Python to solve time series problems.
Book Synopsis Temporal Data Mining by : Theophano Mitsa
Download or read book Temporal Data Mining written by Theophano Mitsa and published by CRC Press. This book was released on 2010-03-10 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: From basic data mining concepts to state-of-the-art advances, this book covers the theory of the subject as well as its application in a variety of fields. It discusses the incorporation of temporality in databases as well as temporal data representation, similarity computation, data classification, clustering, pattern discovery, and prediction. The book also explores the use of temporal data mining in medicine and biomedical informatics, business and industrial applications, web usage mining, and spatiotemporal data mining. Along with various state-of-the-art algorithms, each chapter includes detailed references and short descriptions of relevant algorithms and techniques described in other references.
Book Synopsis Big Data Analytics for Smart Transport and Healthcare Systems by : Saeid Pourroostaei Ardakani
Download or read book Big Data Analytics for Smart Transport and Healthcare Systems written by Saeid Pourroostaei Ardakani and published by Springer Nature. This book was released on 2024-01-04 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to introduce big data solutions in urban sustainability applications—mainly smart transportation and healthcare systems. It focuses on machine learning techniques and data processing approaches which have the capacity to handle/process huge, live, and complex datasets in real-time transportation and healthcare applications. For this, several state-of-the-art data processing approaches including data pre-processing, classification, regression, and clustering are introduced, tested, and evaluated to highlight their benefits and constraints where data is sensitive, real-time, and/or semi-structured.
Book Synopsis Advanced Data Science and Analytics with Python by : Jesus Rogel-Salazar
Download or read book Advanced Data Science and Analytics with Python written by Jesus Rogel-Salazar and published by CRC Press. This book was released on 2020-05-05 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow-up to the topics discussed in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and others. The model development is supported by the use of frameworks such as Keras, TensorFlow and Core ML, as well as Swift for the development of iOS and MacOS applications. Features: Targets readers with a background in programming, who are interested in the tools used in data analytics and data science Uses Python throughout Presents tools, alongside solved examples, with steps that the reader can easily reproduce and adapt to their needs Focuses on the practical use of the tools rather than on lengthy explanations Provides the reader with the opportunity to use the book whenever needed rather than following a sequential path The book can be read independently from the previous volume and each of the chapters in this volume is sufficiently independent from the others, providing flexibility for the reader. Each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book. Time series analysis, natural language processing, topic modelling, social network analysis, neural networks and deep learning are comprehensively covered. The book discusses the need to develop data products and addresses the subject of bringing models to their intended audiences – in this case, literally to the users’ fingertips in the form of an iPhone app. About the Author Dr. Jesús Rogel-Salazar is a lead data scientist in the field, working for companies such as Tympa Health Technologies, Barclays, AKQA, IBM Data Science Studio and Dow Jones. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK.
Book Synopsis Advances in Knowledge Discovery and Management by : Fabrice Guillet
Download or read book Advances in Knowledge Discovery and Management written by Fabrice Guillet and published by Springer. This book was released on 2016-11-03 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a collection of representative and novel work in the field of data mining, knowledge discovery, clustering and classification, based on expanded and reworked versions of a selection of the best papers originally presented in French at the EGC 2014 and EGC 2015 conferences held in Rennes (France) in January 2014 and Luxembourg in January 2015. The book is in three parts: The first four chapters discuss optimization considerations in data mining. The second part explores specific quality measures, dissimilarities and ultrametrics. The final chapters focus on semantics, ontologies and social networks. Written for PhD and MSc students, as well as researchers working in the field, it addresses both theoretical and practical aspects of knowledge discovery and management.