Time Series Analysis and Forecasting [electronic resource] : Selected Contributions from ITISE 2017 /
Rojas, Ignacio.editor.
Pomares, Héctor.editor.
Valenzuela, Olga.editor.
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.
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 Periodic Time-series Data - A Case Study with Electrocardiogram Data -- Similarity Analysis of Time Interval Data Sets - A Graph Theory Approach -- Logical Comparison Measures in Classification of Data -- Econometric Models -- Asymptotic and Bootstrap Tests For a Change in Autoregression Omitting Variability Estimation -- Distance Between VARMA Models and its Application to Spatial Differences Analysis in the Relationship GDP - Unemployment Growth Rate in Europe -- Copulas for Modeling the Relationship between the Inflation and the Exchange Rates -- Energy Time Series Forecasting -- Fuel Consumption Estimation for Climbing Phase -- Time Series Optimization for Energy Prediction in Wi-Fi Infrastructures -- An econometric analysis of the merit order effect in electricity spot price: the Germany case -- Forecasting in Real Problems -- The analysis of variability of short data sets based on Mahalanobis distance calculation and surrogate time series testing -- On generalized additive models with dependent time series covariates -- A Bayesian Approach to Astronomical Time Delay Estimations -- Further Results on a Modified EM Algorithm for Parameter Estimation in Linear Models with Time-Dependent Autoregressive and t-Distributed Errors. .
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.
Statistics .
Econometrics.
Mathematical statistics.
Probabilities.
Statistics for Business, Management, Economics, Finance, Insurance.
Econometrics.
Probability and Statistics in Computer Science.
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
Probability Theory and Stochastic Processes.