Advanced Analytics and Learning on Temporal Data
Advanced Analytics and Learning on Temporal Data

Author: Vincent Lemaire

Publisher: Springer Nature

Published: 2020-01-22

Total Pages: 236

ISBN-13: 3030390985

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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.

Advanced Analytics and Learning on Temporal Data
Advanced Analytics and Learning on Temporal Data

Author: Thomas Guyet

Publisher: Springer Nature

Published: 2023-03-20

Total Pages: 209

ISBN-13: 3031243781

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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.

Advanced Analytics and Learning on Temporal Data
Advanced Analytics and Learning on Temporal Data

Author: Georgiana Ifrim

Publisher: Springer Nature

Published: 2024-01-20

Total Pages: 315

ISBN-13: 3031498968

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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.

Advanced Analytics and Learning on Temporal Data
Advanced Analytics and Learning on Temporal Data

Author: Vincent Lemaire

Publisher: Springer Nature

Published: 2021-12-02

Total Pages: 202

ISBN-13: 3030914453

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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.

Advanced Analysis and Learning on Temporal Data
Advanced Analysis and Learning on Temporal Data

Author: Ahlame Douzal-Chouakria

Publisher: Springer

Published: 2016-08-03

Total Pages: 180

ISBN-13: 3319444123

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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.

Advanced Analytics with Spark
Advanced Analytics with Spark

Author: Sandy Ryza

Publisher: "O'Reilly Media, Inc."

Published: 2015-04-02

Total Pages: 290

ISBN-13: 1491912715

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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

Advanced Data Analytics Using Python
Advanced Data Analytics Using Python

Author: Sayan Mukhopadhyay

Publisher: Apress

Published: 2018-03-29

Total Pages: 195

ISBN-13: 1484234502

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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.

Big Data Application in Power Systems
Big Data Application in Power Systems

Author: Reza Arghandeh

Publisher: Elsevier

Published: 2017-11-27

Total Pages: 482

ISBN-13: 0128119691

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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

Applied Time Series Analysis and Forecasting with Python
Applied Time Series Analysis and Forecasting with Python

Author: Changquan Huang

Publisher: Springer Nature

Published: 2022-10-19

Total Pages: 377

ISBN-13: 3031135849

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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.

Advanced Data Analytics for Power Systems
Advanced Data Analytics for Power Systems

Author: Ali Tajer

Publisher: Cambridge University Press

Published: 2021-04-08

Total Pages: 601

ISBN-13: 1108494757

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Experts in data analytics and power engineering present techniques addressing the needs of modern power systems, covering theory and applications related to power system reliability, efficiency, and security. With topics spanning large-scale and distributed optimization, statistical learning, big data analytics, graph theory, and game theory, this is an essential resource for graduate students and researchers in academia and industry with backgrounds in power systems engineering, applied mathematics, and computer science.