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Record Nr. |
UNINA9910254304803321 |
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Titolo |
Advances in Time Series Analysis and Forecasting : Selected Contributions from ITISE 2016 / / edited by Ignacio Rojas, Héctor Pomares, Olga Valenzuela |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
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ISBN |
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Edizione |
[1st ed. 2017.] |
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Descrizione fisica |
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1 online resource (412 pages) |
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Collana |
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Contributions to Statistics, , 2628-8966 |
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Disciplina |
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Soggetti |
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Statistics |
Econometrics |
Computer science - Mathematics |
Mathematical statistics |
Probabilities |
Statistics in Business, Management, Economics, Finance, Insurance |
Probability and Statistics in Computer Science |
Probability Theory |
Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters and index. |
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Nota di contenuto |
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Preface -- Part I: Analysis of Irregularly Sampled Time Series: Techniques, Algorithms and Case Studies -- Scientific Contributions -- Part II: Multi-scale Analysis of Univariate and Multivariate Time Series -- Scientific Contributions -- Part III: Linear and Non-linear Time Series Models -- Scientific Contributions -- Part IV: Advanced Time Series Forecasting Methods -- Scientific Contributions -- Part V: Applications in Time Series Analysis and Forecasting -- Scientific Contributions -- Author Index. |
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Sommario/riassunto |
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This volume of selected and peer-reviewed contributions on the latest developments in time series analysis and forecasting updates the reader on topics such as analysis of irregularly sampled time |
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series, multi-scale analysis of univariate and multivariate time series, linear and non-linear time series models, advanced time series forecasting methods, applications in time series analysis and forecasting, advanced methods and online learning in time series and high-dimensional and complex/big data time series. The contributions were originally presented at the International Work-Conference on Time Series, ITISE 2016, held in Granada, Spain, June 27-29, 2016. The series of ITISE conferences provides a forum for scientists, engineers, educators and students to discuss the latest ideas and implementations in the foundations, theory, models and applications in the field of time series analysis and forecasting. It focuses on interdisciplinary and multidisciplinary rese arch encompassing the disciplines of computer science, mathematics, statistics and econometrics. |
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