Time Series Analysis and Forecasting: Selected Contributions from the ITISE Conference

✍ Scribed by Ignacio Rojas, Héctor Pomares (eds.)

• Publisher: Springer International Publishing
• Year: 2016
• Tongue: English
• Leaves: 384
• Series: Contributions to Statistics
• Edition: 1
• Category: Library

✦ Synopsis

This volume presents selected peer-reviewed contributions from The International Work-Conference on Time Series, ITISE 2015, held in Granada, Spain, July 1-3, 2015. It discusses topics in time series analysis and forecasting, advanced methods and online learning in time series, high-dimensional and complex/big data time series as well as forecasting in real problems.

The International Work-Conferences on Time Series (ITISE) provide 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 the disciplines of computer science, mathematics, statistics and econometrics.

✦ Table of Contents

Front Matter....Pages i-xix
Front Matter....Pages 1-1
A Direct Method for the Langevin-Analysis of Multidimensional Stochastic Processes with Strong Correlated Measurement Noise....Pages 3-11
Threshold Autoregressive Models for Directional Time Series....Pages 13-25
Simultaneous Statistical Inference in Dynamic Factor Models....Pages 27-45
The Relationship Between the Beveridge–Nelson Decomposition and Exponential Smoothing....Pages 47-60
Permutation Entropy and Order Patterns in Long Time Series....Pages 61-73
Generative Exponential Smoothing and Generative ARMA Models to Forecast Time-Variant Rates or Probabilities....Pages 75-88
First-Passage Time Properties of Correlated Time Series with Scale-Invariant Behavior and with Crossovers in the Scaling....Pages 89-102
Front Matter....Pages 103-103
The Environmental Impact of Economic Activity on the Planet....Pages 105-112
Stock Indices in Emerging and Consolidated Economies from a Fractal Perspective....Pages 113-122
Value at Risk with Filtered Historical Simulation....Pages 123-133
A SVEC Model to Forecast and Perform Structural Analysis (Shocks) for the Mexican Economy, 1985Q1–2014Q4....Pages 135-145
Intraday Data vs Daily Data to Forecast Volatility in Financial Markets....Pages 147-159
Predictive and Descriptive Qualities of Different Classes of Models for Parallel Economic Development of Selected EU-Countries....Pages 161-173
Search and Evaluation of Stock Ranking Rules Using Internet Activity Time Series and Multiobjective Genetic Programming....Pages 175-187
Integer-Valued APARCH Processes....Pages 189-202
Front Matter....Pages 203-203
Emergency-Related, Social Network Time Series: Description and Analysis....Pages 205-215
Competitive Models for the Spanish Short-Term Electricity Demand Forecasting....Pages 217-231
Age-Specific Death Rates Smoothed by the Gompertz–Makeham Function and Their Application in Projections by Lee–Carter Model....Pages 233-245
An Application of Time Series Analysis in Judging the Working State of Ground-Based Microwave Radiometers and Data Calibration....Pages 247-259
Identifying the Best Performing Time Series Analytics for Sea Level Research....Pages 261-278
Front Matter....Pages 203-203
Modellation and Forecast of Traffic Series by a Stochastic Process....Pages 279-292
Spatio-Temporal Modeling for fMRI Data....Pages 293-311
Front Matter....Pages 313-313
Communicating Artificial Neural Networks with Physical-Based Flow Model for Complex Coastal Systems....Pages 315-327
Forecasting Daily Water Demand Using Fuzzy Cognitive Maps....Pages 329-340
Forecasting Short-Term Demand for Electronic Assemblies by Using Soft-Rules....Pages 341-353
Electrical Load Forecasting: A Parallel Seasonal Approach....Pages 355-366
A Compounded Multi-resolution-Artificial Neural Network Method for the Prediction of Time Series with Complex Dynamics....Pages 367-384

✦ Subjects

Statistics for Business/Economics/Mathematical Finance/Insurance; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Econometrics; Probability and Statistics in Computer Science