Ebook: Time Series Analysis, Modeling and Applications: A Computational Intelligence Perspective
- Tags: Computational Intelligence, Artificial Intelligence (incl. Robotics)
- Series: Intelligent Systems Reference Library 47
- Year: 2013
- Publisher: Springer-Verlag Berlin Heidelberg
- Edition: 1
- Language: English
- pdf
Temporal and spatiotemporal data form an inherent fabric of the society as we are faced with streams of data coming from numerous sensors, data feeds, recordings associated with numerous areas of application embracing physical and human-generated phenomena (environmental data, financial markets, Internet activities, etc.). A quest for a thorough analysis, interpretation, modeling and prediction of time series comes with an ongoing challenge for developing models that are both accurate and user-friendly (interpretable).
The volume is aimed to exploit the conceptual and algorithmic framework of Computational Intelligence (CI) to form a cohesive and comprehensive environment for building models of time series. The contributions covered in the volume are fully reflective of the wealth of the CI technologies by bringing together ideas, algorithms, and numeric studies, which convincingly demonstrate their relevance, maturity and visible usefulness. It reflects upon the truly remarkable diversity of methodological and algorithmic approaches and case studies.
This volume is aimed at a broad audience of researchers and practitioners engaged in various branches of operations research, management, social sciences, engineering, and economics. Owing to the nature of the material being covered and a way it has been arranged, it establishes a comprehensive and timely picture of the ongoing pursuits in the area and fosters further developments.
Temporal and spatiotemporal data form an inherent fabric of the society as we are faced with streams of data coming from numerous sensors, data feeds, recordings associated with numerous areas of application embracing physical and human-generated phenomena (environmental data, financial markets, Internet activities, etc.). A quest for a thorough analysis, interpretation, modeling and prediction of time series comes with an ongoing challenge for developing models that are both accurate and user-friendly (interpretable).
The volume is aimed to exploit the conceptual and algorithmic framework of Computational Intelligence (CI) to form a cohesive and comprehensive environment for building models of time series. The contributions covered in the volume are fully reflective of the wealth of the CI technologies by bringing together ideas, algorithms, and numeric studies, which convincingly demonstrate their relevance, maturity and visible usefulness. It reflects upon the truly remarkable diversity of methodological and algorithmic approaches and case studies.
This volume is aimed at a broad audience of researchers and practitioners engaged in various branches of operations research, management, social sciences, engineering, and economics. Owing to the nature of the material being covered and a way it has been arranged, it establishes a comprehensive and timely picture of the ongoing pursuits in the area and fosters further developments.
Temporal and spatiotemporal data form an inherent fabric of the society as we are faced with streams of data coming from numerous sensors, data feeds, recordings associated with numerous areas of application embracing physical and human-generated phenomena (environmental data, financial markets, Internet activities, etc.). A quest for a thorough analysis, interpretation, modeling and prediction of time series comes with an ongoing challenge for developing models that are both accurate and user-friendly (interpretable).
The volume is aimed to exploit the conceptual and algorithmic framework of Computational Intelligence (CI) to form a cohesive and comprehensive environment for building models of time series. The contributions covered in the volume are fully reflective of the wealth of the CI technologies by bringing together ideas, algorithms, and numeric studies, which convincingly demonstrate their relevance, maturity and visible usefulness. It reflects upon the truly remarkable diversity of methodological and algorithmic approaches and case studies.
This volume is aimed at a broad audience of researchers and practitioners engaged in various branches of operations research, management, social sciences, engineering, and economics. Owing to the nature of the material being covered and a way it has been arranged, it establishes a comprehensive and timely picture of the ongoing pursuits in the area and fosters further developments.
Content:
Front Matter....Pages 1-7
The Links between Statistical and Fuzzy Models for Time Series Analysis and Forecasting....Pages 1-30
Incomplete Time Series: Imputation through Genetic Algorithms....Pages 31-52
Intelligent Aggregation and Time Series Smoothing....Pages 53-75
Financial Fuzzy Time Series Models Based on Ordered Fuzzy Numbers....Pages 77-95
Stochastic-Fuzzy Knowledge-Based Approach to Temporal Data Modeling....Pages 97-118
A Novel Choquet Integral Composition Forecasting Model for Time Series Data Based on Completed Extensional L-Measure....Pages 119-137
An Application of Enhanced Knowledge Models to Fuzzy Time Series....Pages 139-175
A Wavelet Transform Approach to Chaotic Short-Term Forecasting....Pages 177-197
Fuzzy Forecasting with Fractal Analysis for the Time Series of Environmental Pollution....Pages 199-213
Support Vector Regression with Kernel Mahalanobis Measure for Financial Forecast....Pages 215-227
Neural Networks and Wavelet De-Noising for Stock Trading and Prediction....Pages 229-247
Channel and Class Dependent Time-Series Embedding Using Partial Mutual Information Improves Sensorimotor Rhythm Based Brain-Computer Interfaces....Pages 249-278
How to Describe and Propagate Uncertainty When Processing Time Series: Metrological and Computational Challenges, with Potential Applications to Environmental Studies....Pages 279-299
Building a Rough Sets-Based Prediction Model of Tick-Wise Stock Price Fluctuations....Pages 301-329
A Best-Match Forecasting Model for High-Order Fuzzy Time Series....Pages 331-345
Building Fuzzy Autocorrelation Model and Its Application to the Analysis of Stock Price Time-Series Data....Pages 347-367
Predicting Hourly Ozone Concentration Time Series in Dali Area of Taichung City Based on Seven Types of GM (1, 1) Model....Pages 369-383
Nonlinear Time Series Prediction of Atmospheric Visibility in Shanghai....Pages 385-399
Back Matter....Pages 0--1
Temporal and spatiotemporal data form an inherent fabric of the society as we are faced with streams of data coming from numerous sensors, data feeds, recordings associated with numerous areas of application embracing physical and human-generated phenomena (environmental data, financial markets, Internet activities, etc.). A quest for a thorough analysis, interpretation, modeling and prediction of time series comes with an ongoing challenge for developing models that are both accurate and user-friendly (interpretable).
The volume is aimed to exploit the conceptual and algorithmic framework of Computational Intelligence (CI) to form a cohesive and comprehensive environment for building models of time series. The contributions covered in the volume are fully reflective of the wealth of the CI technologies by bringing together ideas, algorithms, and numeric studies, which convincingly demonstrate their relevance, maturity and visible usefulness. It reflects upon the truly remarkable diversity of methodological and algorithmic approaches and case studies.
This volume is aimed at a broad audience of researchers and practitioners engaged in various branches of operations research, management, social sciences, engineering, and economics. Owing to the nature of the material being covered and a way it has been arranged, it establishes a comprehensive and timely picture of the ongoing pursuits in the area and fosters further developments.
Content:
Front Matter....Pages 1-7
The Links between Statistical and Fuzzy Models for Time Series Analysis and Forecasting....Pages 1-30
Incomplete Time Series: Imputation through Genetic Algorithms....Pages 31-52
Intelligent Aggregation and Time Series Smoothing....Pages 53-75
Financial Fuzzy Time Series Models Based on Ordered Fuzzy Numbers....Pages 77-95
Stochastic-Fuzzy Knowledge-Based Approach to Temporal Data Modeling....Pages 97-118
A Novel Choquet Integral Composition Forecasting Model for Time Series Data Based on Completed Extensional L-Measure....Pages 119-137
An Application of Enhanced Knowledge Models to Fuzzy Time Series....Pages 139-175
A Wavelet Transform Approach to Chaotic Short-Term Forecasting....Pages 177-197
Fuzzy Forecasting with Fractal Analysis for the Time Series of Environmental Pollution....Pages 199-213
Support Vector Regression with Kernel Mahalanobis Measure for Financial Forecast....Pages 215-227
Neural Networks and Wavelet De-Noising for Stock Trading and Prediction....Pages 229-247
Channel and Class Dependent Time-Series Embedding Using Partial Mutual Information Improves Sensorimotor Rhythm Based Brain-Computer Interfaces....Pages 249-278
How to Describe and Propagate Uncertainty When Processing Time Series: Metrological and Computational Challenges, with Potential Applications to Environmental Studies....Pages 279-299
Building a Rough Sets-Based Prediction Model of Tick-Wise Stock Price Fluctuations....Pages 301-329
A Best-Match Forecasting Model for High-Order Fuzzy Time Series....Pages 331-345
Building Fuzzy Autocorrelation Model and Its Application to the Analysis of Stock Price Time-Series Data....Pages 347-367
Predicting Hourly Ozone Concentration Time Series in Dali Area of Taichung City Based on Seven Types of GM (1, 1) Model....Pages 369-383
Nonlinear Time Series Prediction of Atmospheric Visibility in Shanghai....Pages 385-399
Back Matter....Pages 0--1
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