Time Series Analysis and Forecasting : Selected Contributions from ITISE 2017. 1st ed. 2018

種類:

電子ブック

責任表示:

edited by Ignacio Rojas, Héctor Pomares, Olga Valenzuela

出版情報:

Cham : Springer International Publishing : Imprint: Springer, 2018

著者名:

• Rojas, Ignacio.
• Pomares, Héctor.
• Valenzuela, Olga.
• SpringerLink (Online service)

シリーズ名:

Contributions to Statistics ;

ISBN:

9783319969442 [3319969447]    

注記:

Preface -- Advanced Mathematical Methodologies in Time Series -- Forecasting via Fokker-Planck using conditional probabilities -- Cryptanalysis of a Random Number Generator Based on a Chaotic Ring Oscillator -- Further Results on Robust Multivariate Time Series Analysis in Nonlinear Models with Autoregressive and t-Distributed Errors -- A New Estimation Technique for AR(1) Model with Long-tailed Symmetric Innovations -- Prediction of High-Dimensional Time Series with Exogenous Variables Using Generalized Koopman Operator Framework in Reproducing Kernel Hilbert Space -- Eigenvalues distribution limit of covariance matrices with AR processes entries -- Computational Intelligence Methods for Time Series -- Deep Learning for Detection of BGP Anomalies -- Using Scaling Methods to Improve Support Vector Regression's Performance for Travel Time and Traffic Volume Predictions -- Dimensionality Reduction and Similarity Measures in Time Series -- Linear Trend Filtering via Adaptive Lasso -- Detecting Discords in Quasi
This book presents selected peer-reviewed contributions from the International Work-Conference on Time Series, ITISE 2017, held in Granada, Spain, September 18-20, 2017. It discusses topics in time series analysis and forecasting, including advanced mathematical methodology, computational intelligence methods for time series, dimensionality reduction and similarity measures, econometric models, energy time series forecasting, forecasting in real problems, online learning in time series as well as high-dimensional and complex/big data time series. 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 research encompassing computer science, mathematics, statistics and econometrics.

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