Theory and applications of time series analysis : selected contributions from ITISE 2019
著者
書誌事項
Theory and applications of time series analysis : selected contributions from ITISE 2019
（Contributions to statistics）
Springer, c2020
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注記
Includes bibliographical refrences and index
内容説明・目次
内容説明
This book presents a selection of peer-reviewed contributions on the latest advances in time series analysis, presented at the International Conference on Time Series and Forecasting (ITISE 2019), held in Granada, Spain, on September 25-27, 2019. The first two parts of the book present theoretical contributions on statistical and advanced mathematical methods, and on econometric models, financial forecasting and risk analysis. The remaining four parts include practical contributions on time series analysis in energy; complex/big data time series and forecasting; time series analysis with computational intelligence; and time series analysis and prediction for other real-world problems. Given this mix of topics, readers will acquire a more comprehensive perspective on the field of time series analysis and forecasting.
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.
目次
Part I: Advanced Statistical and Mathematical Methods for Time Series Analysis.- Random Forest Variable Selection for Sparse Vector Autoregressive Models (Dmitry Pavlyuk).- Covariance functions for Gaussian Laplacian fields in higher dimension (Gyorgy Terdik).- The Correspondence between Stochastic Linear Diference and Diferential Equations (D. Stephen G. Pollock).- New test for a random walk detection based on the arcsine law (Konrad Furmanczyk, Marcin Dudzinski and Arkadiusz Orlowski).- Part II: Econometric Models and Forecasting.- On the automatic identification of Unobserved Components Models (Diego J. Pedregal and Juan R. Trapero).- Spatial integration of pig meat markets in the EU: Complex Network analysis of nonlinear price relationships (Christos Emmanouilides and Alexej Proskynitopoulos).- Comparative Study of Models for Forecasting Nigerian Stock Exchange Market Capitalization (Nura Isah, Basiru Yusuf and Sani I.S. Doguwa).- Industry Specifics of Models Predicting Financial Distress (Dagmar Camska).- Stochastic volatility model's predictive relevance for Equity Markets (Per B Solibakke).- Empirical test of the Balassa-Samuelson Effect in Selected African Countries (Joel Hinaunye Eita, Zitsile Zamantungwa Khumalo and Ireen Choga).- Part III: Energy Time Series Forecasting.- End of charge detection of batteries with high production tolerances (Andre Loechte, Ole Gebert and Peter Gloesekoetter).- The effect of Daylight Saving Time on Spanish Electrical Consumption (Eduardo Caro Huertas, Jesus Juan Ruiz, Marta Mana Sanchez, Jesus Ruperez Aguilera, Carlos Rodriguez Huidobro, Ana Rodriguez Aparicio and Juan Jose Abellan Perez).- Wind Speed Forecasting Using Kernel Ridge Regression (Mohammad Alalami, Maher Maalouf and Tarek El Fouly).- Applying a 1D-CNN Network to Electricity Load Forecasting (Christian Lang, Florian Steinborn, Oliver Steffens and Elmar W. Lang).- Long and Short Term Prediction of Power Consumption using LSTM Networks (Juan Carlos Morales, Salvador Moreno, Carlos Bailon, Hector Pomares, Ignacio Rojas and Luis Javier Herrera).- Part IV: Forecasting Complex/Big data problems.- Freedman's Paradox: a Solution Based on Normalized Entropy (Pedro Macedo).- Mining News Data for the Measurement and Prediction of Inflation Expectations (Diana Gabrielyan, Lenno Uuskula and Jaan Masso).- Big Data: Forecasting and Control for Tourism Demand (Miguel Angel Ruiz Reina).- Traffic Networks via Neural Networks: Description and Evolution (Alexandros Sopasakis).- Part V: Time Series Analysis with Computational Intelligence.- A Comparative Study on Machine Learning Techniques for Intense Convective Rainfall Events Forecasting (Matteo Sangiorgio, Stefano Barindelli, Valerio Guglieri, Riccardo Biondi, Enrico Solazzo, Eugenio Realini, Giovanna Venuti and Giorgio Guariso).- Long-Short Term Memory Networks for the Prediction of Transformer Temperature for Energy Distribution Smart Grids (Francisco Jesus Martinez-Murcia, Javier Ramirez, Fermin Segovia, Andres Ortiz, Susana Carrillo, Javier Leiva, Jacob Rodriguez-Rivero and Juan Manuel Gorriz).- Deep Multilayer Perceptron for Knowledge Extraction: Understanding the Gardon de Mialet Flash Floods Modelling (Bob E. Saint Fleur, Guillaume Artigue, Anne Johannet and Severin Pistre).- Forecasting short-term and medium-term time series:a comparison of artificial neural networks and fuzzy models (Tatiana Afanasieva and Pavel Platov).- Inflation Rate Forecasting: Extreme Learning Machine as a Model Combination Method (Jeronymo Marcondes Pinto and Emerson Fernandes Marcal).- Part VI: Time Series Analysis and Prediction in Other Real Problems.- Load Forecast by Multi Task Learning Models: designed for a new collaborative world (Leontina Pinto, Jacques Szczupak and Robinson Semolini).- Power transformer forecasting in smart grids using NARX neural networks (Javier Ramirez, Francisco J. Martinez Murcia, Fermin Segovia, Andres Ortiz, Diego Salas-Gonz_alez, Susana Carrillo, Javier Leiva, Jacob Rodriguez- Rivero and Juan M. Gorriz).- Short term forecast of emergency departements visits through calendar selection (Cosimo Lovecchio, Mauro Tucci, Sami Barmada, Andrea Serafini, Luigi Bechi, Mauro Breggia, Simona Dei and Daniela Matarrese).- Discordant Observation Modelling (Sonya Leech and Bojan Bozic).- Applying Diebold-Mariano test for performance evaluation between individual and hybrid time series models for modeling bivariate time series data and forecasting the unemployment rate in the USA (Moamen Abbas Mousa Al-Sharifi and Firas Ahmmed Mohammed Al-Mohana).
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