Author: Ahlame Douzal-Chouakria
Publisher: Springer
ISBN: 3319444123
Category : Computers
Languages : en
Pages : 180
Book Description
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 Analysis and Learning on Temporal Data
Author: Ahlame Douzal-Chouakria
Publisher: Springer
ISBN: 3319444123
Category : Computers
Languages : en
Pages : 180
Book Description
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.
Publisher: Springer
ISBN: 3319444123
Category : Computers
Languages : en
Pages : 180
Book Description
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 and Learning on Temporal Data
Author: Vincent Lemaire
Publisher: Springer Nature
ISBN: 3030914453
Category : Computers
Languages : en
Pages : 202
Book Description
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.
Publisher: Springer Nature
ISBN: 3030914453
Category : Computers
Languages : en
Pages : 202
Book Description
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 Analytics and Learning on Temporal Data
Author: Vincent Lemaire
Publisher: Springer Nature
ISBN: 3030657426
Category : Computers
Languages : en
Pages : 240
Book Description
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.
Publisher: Springer Nature
ISBN: 3030657426
Category : Computers
Languages : en
Pages : 240
Book Description
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.
Advanced Analytics and Learning on Temporal Data
Author: Georgiana Ifrim
Publisher: Springer Nature
ISBN: 3031498968
Category : Computers
Languages : en
Pages : 315
Book Description
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.
Publisher: Springer Nature
ISBN: 3031498968
Category : Computers
Languages : en
Pages : 315
Book Description
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
Author: Thomas Guyet
Publisher: Springer Nature
ISBN: 3031243781
Category : Computers
Languages : en
Pages : 209
Book Description
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.
Publisher: Springer Nature
ISBN: 3031243781
Category : Computers
Languages : en
Pages : 209
Book Description
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.
Theory and Applications of Time Series Analysis and Forecasting
Author: Olga Valenzuela
Publisher: Springer Nature
ISBN: 3031141970
Category : Mathematics
Languages : en
Pages : 331
Book Description
This book presents a selection of peer-reviewed contributions on the latest developments in time series analysis and forecasting, presented at the 7th International Conference on Time Series and Forecasting, ITISE 2021, held in Gran Canaria, Spain, July 19-21, 2021. It is divided into four parts. The first part addresses general modern methods and theoretical aspects of time series analysis and forecasting, while the remaining three parts focus on forecasting methods in econometrics, time series forecasting and prediction, and numerous other real-world applications. Covering a broad range of topics, the book will give readers a modern perspective on the subject. The ITISE conference series provides a forum for scientists, engineers, educators and students to discuss the latest advances and implementations in the foundations, theory, models and applications of time series analysis and forecasting. It focuses on interdisciplinary research encompassing computer science, mathematics, statistics and econometrics.
Publisher: Springer Nature
ISBN: 3031141970
Category : Mathematics
Languages : en
Pages : 331
Book Description
This book presents a selection of peer-reviewed contributions on the latest developments in time series analysis and forecasting, presented at the 7th International Conference on Time Series and Forecasting, ITISE 2021, held in Gran Canaria, Spain, July 19-21, 2021. It is divided into four parts. The first part addresses general modern methods and theoretical aspects of time series analysis and forecasting, while the remaining three parts focus on forecasting methods in econometrics, time series forecasting and prediction, and numerous other real-world applications. Covering a broad range of topics, the book will give readers a modern perspective on the subject. The ITISE conference series provides a forum for scientists, engineers, educators and students to discuss the latest advances and implementations in the foundations, theory, models and applications of time series analysis and forecasting. It focuses on interdisciplinary research encompassing computer science, mathematics, statistics and econometrics.
Multitemporal Earth Observation Image Analysis
Author: Clément Mallet
Publisher: John Wiley & Sons
ISBN: 1789451760
Category : Technology & Engineering
Languages : en
Pages : 276
Book Description
Earth observation has witnessed a unique paradigm change in the last decade with a diverse and ever-growing number of data sources. Among them, time series of remote sensing images has proven to be invaluable for numerous environmental and climate studies. Multitemporal Earth Observation Image Analysis provides illustrations of recent methodological advances in data processing and information extraction from imagery, with an emphasis on the temporal dimension uncovered either by recent satellite constellations (in particular the Sentinels from the European Copernicus programme) or archival aerial images available in national archives. The book shows how complementary data sources can be efficiently used, how spatial and temporal information can be leveraged for biophysical parameter estimation, classification of land surfaces and object tracking, as well as how standard machine learning and state-of-the-art deep learning solutions can solve complex problems with real-world applications.
Publisher: John Wiley & Sons
ISBN: 1789451760
Category : Technology & Engineering
Languages : en
Pages : 276
Book Description
Earth observation has witnessed a unique paradigm change in the last decade with a diverse and ever-growing number of data sources. Among them, time series of remote sensing images has proven to be invaluable for numerous environmental and climate studies. Multitemporal Earth Observation Image Analysis provides illustrations of recent methodological advances in data processing and information extraction from imagery, with an emphasis on the temporal dimension uncovered either by recent satellite constellations (in particular the Sentinels from the European Copernicus programme) or archival aerial images available in national archives. The book shows how complementary data sources can be efficiently used, how spatial and temporal information can be leveraged for biophysical parameter estimation, classification of land surfaces and object tracking, as well as how standard machine learning and state-of-the-art deep learning solutions can solve complex problems with real-world applications.
Advanced Analytics with Spark
Author: Sandy Ryza
Publisher: "O'Reilly Media, Inc."
ISBN: 1491912715
Category : Computers
Languages : en
Pages : 290
Book Description
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
Publisher: "O'Reilly Media, Inc."
ISBN: 1491912715
Category : Computers
Languages : en
Pages : 290
Book Description
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
On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory
Author: Fabian Guignard
Publisher: Springer Nature
ISBN: 3030952312
Category : Science
Languages : en
Pages : 170
Book Description
The gathering and storage of data indexed in space and time are experiencing unprecedented growth, demanding for advanced and adapted tools to analyse them. This thesis deals with the exploration and modelling of complex high-frequency and non-stationary spatio-temporal data. It proposes an efficient framework in modelling with machine learning algorithms spatio-temporal fields measured on irregular monitoring networks, accounting for high dimensional input space and large data sets. The uncertainty quantification is enabled by specifying this framework with the extreme learning machine, a particular type of artificial neural network for which analytical results, variance estimation and confidence intervals are developed. Particular attention is also paid to a highly versatile exploratory data analysis tool based on information theory, the Fisher-Shannon analysis, which can be used to assess the complexity of distributional properties of temporal, spatial and spatio-temporal data sets. Examples of the proposed methodologies are concentrated on data from environmental sciences, with an emphasis on wind speed modelling in complex mountainous terrain and the resulting renewable energy assessment. The contributions of this thesis can find a large number of applications in several research domains where exploration, understanding, clustering, interpolation and forecasting of complex phenomena are of utmost importance.
Publisher: Springer Nature
ISBN: 3030952312
Category : Science
Languages : en
Pages : 170
Book Description
The gathering and storage of data indexed in space and time are experiencing unprecedented growth, demanding for advanced and adapted tools to analyse them. This thesis deals with the exploration and modelling of complex high-frequency and non-stationary spatio-temporal data. It proposes an efficient framework in modelling with machine learning algorithms spatio-temporal fields measured on irregular monitoring networks, accounting for high dimensional input space and large data sets. The uncertainty quantification is enabled by specifying this framework with the extreme learning machine, a particular type of artificial neural network for which analytical results, variance estimation and confidence intervals are developed. Particular attention is also paid to a highly versatile exploratory data analysis tool based on information theory, the Fisher-Shannon analysis, which can be used to assess the complexity of distributional properties of temporal, spatial and spatio-temporal data sets. Examples of the proposed methodologies are concentrated on data from environmental sciences, with an emphasis on wind speed modelling in complex mountainous terrain and the resulting renewable energy assessment. The contributions of this thesis can find a large number of applications in several research domains where exploration, understanding, clustering, interpolation and forecasting of complex phenomena are of utmost importance.
Statistical Learning for Big Dependent Data
Author: Daniel Peña
Publisher: John Wiley & Sons
ISBN: 1119417384
Category : Mathematics
Languages : en
Pages : 562
Book Description
Master advanced topics in the analysis of large, dynamically dependent datasets with this insightful resource Statistical Learning with Big Dependent Data delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzing and forecasting large and dynamically dependent data sets. The book presents automatic procedures for modelling and forecasting large sets of time series data. Beginning with some visualization tools, the book discusses procedures and methods for finding outliers, clusters, and other types of heterogeneity in big dependent data. It then introduces various dimension reduction methods, including regularization and factor models such as regularized Lasso in the presence of dynamical dependence and dynamic factor models. The book also covers other forecasting procedures, including index models, partial least squares, boosting, and now-casting. It further presents machine-learning methods, including neural network, deep learning, classification and regression trees and random forests. Finally, procedures for modelling and forecasting spatio-temporal dependent data are also presented. Throughout the book, the advantages and disadvantages of the methods discussed are given. The book uses real-world examples to demonstrate applications, including use of many R packages. Finally, an R package associated with the book is available to assist readers in reproducing the analyses of examples and to facilitate real applications. Analysis of Big Dependent Data includes a wide variety of topics for modeling and understanding big dependent data, like: New ways to plot large sets of time series An automatic procedure to build univariate ARMA models for individual components of a large data set Powerful outlier detection procedures for large sets of related time series New methods for finding the number of clusters of time series and discrimination methods , including vector support machines, for time series Broad coverage of dynamic factor models including new representations and estimation methods for generalized dynamic factor models Discussion on the usefulness of lasso with time series and an evaluation of several machine learning procedure for forecasting large sets of time series Forecasting large sets of time series with exogenous variables, including discussions of index models, partial least squares, and boosting. Introduction of modern procedures for modeling and forecasting spatio-temporal data Perfect for PhD students and researchers in business, economics, engineering, and science: Statistical Learning with Big Dependent Data also belongs to the bookshelves of practitioners in these fields who hope to improve their understanding of statistical and machine learning methods for analyzing and forecasting big dependent data.
Publisher: John Wiley & Sons
ISBN: 1119417384
Category : Mathematics
Languages : en
Pages : 562
Book Description
Master advanced topics in the analysis of large, dynamically dependent datasets with this insightful resource Statistical Learning with Big Dependent Data delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzing and forecasting large and dynamically dependent data sets. The book presents automatic procedures for modelling and forecasting large sets of time series data. Beginning with some visualization tools, the book discusses procedures and methods for finding outliers, clusters, and other types of heterogeneity in big dependent data. It then introduces various dimension reduction methods, including regularization and factor models such as regularized Lasso in the presence of dynamical dependence and dynamic factor models. The book also covers other forecasting procedures, including index models, partial least squares, boosting, and now-casting. It further presents machine-learning methods, including neural network, deep learning, classification and regression trees and random forests. Finally, procedures for modelling and forecasting spatio-temporal dependent data are also presented. Throughout the book, the advantages and disadvantages of the methods discussed are given. The book uses real-world examples to demonstrate applications, including use of many R packages. Finally, an R package associated with the book is available to assist readers in reproducing the analyses of examples and to facilitate real applications. Analysis of Big Dependent Data includes a wide variety of topics for modeling and understanding big dependent data, like: New ways to plot large sets of time series An automatic procedure to build univariate ARMA models for individual components of a large data set Powerful outlier detection procedures for large sets of related time series New methods for finding the number of clusters of time series and discrimination methods , including vector support machines, for time series Broad coverage of dynamic factor models including new representations and estimation methods for generalized dynamic factor models Discussion on the usefulness of lasso with time series and an evaluation of several machine learning procedure for forecasting large sets of time series Forecasting large sets of time series with exogenous variables, including discussions of index models, partial least squares, and boosting. Introduction of modern procedures for modeling and forecasting spatio-temporal data Perfect for PhD students and researchers in business, economics, engineering, and science: Statistical Learning with Big Dependent Data also belongs to the bookshelves of practitioners in these fields who hope to improve their understanding of statistical and machine learning methods for analyzing and forecasting big dependent data.