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Pattern Recognition and Classification in Time Series Data

Book Description

Patterns can be any number of items that occur repeatedly, whether in the behaviour of animals, humans, traffic, or even in the appearance of a design. As technologies continue to advance, recognizing, mimicking, and responding to all types of patterns becomes more precise. Pattern Recognition and Classification in Time Series Data focuses on intelligent methods and techniques for recognizing and storing dynamic patterns. Emphasizing topics related to artificial intelligence, pattern management, and algorithm development, in addition to practical examples and applications, this publication is an essential reference source for graduate students, researchers, and professionals in a variety of computer-related disciplines.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Book Series
    1. Mission
    2. Coverage
  5. Preface
  6. Chapter 1: Recognition of Patterns with Fractal Structure in Time Series
    1. ABSTRACT
    2. INTRODUCTION
    3. THE ELLIOTT WAVE-PRINCIPLE
    4. THE BASIC PATTERN OF IMPULSE PHASE
    5. THE BASIC PATTERN OF CORRECTIVE PHASE
    6. BACKPROPAGATION NEURAL NETWORKS
    7. MULTI-CLASSIFIER
    8. PREDICTION CLASSIFIER
    9. METHODOLOGY OF RECOGNITION OF STRUCTURES WITH FRACTAL DYNAMICS
    10. COMPARATIVE STUDY
    11. CONCLUSION
    12. ACKNOWLEDGMENT
    13. REFERENCES
    14. KEY TERMS AND DEFINITIONS
  7. Chapter 2: Introduction to Time Series
    1. ABSTRACT
    2. INTRODUCTION
    3. NOTION OF TIME SERIES
    4. STATIONARITY TIMES SERIES
    5. IMPORTANT TYPES OF DETERMINISTIC TRENDS
    6. IMPORTANT CATEGORIES OF STOCHASTIC MODELS
    7. BASIC OF FORECASTING
    8. CONCLUSION
    9. REFERENCES
    10. KEY TERMS AND DEFINITIONS
  8. Chapter 3: Artificial Intelligence Algorithms for Classification and Pattern Recognition
    1. ABSTRACT
    2. INTRODUCTION
    3. OVERVIEW OF CLASSIFICATION AND PATTERN RECOGNITION TECHNIQUES
    4. ROBUSTNESS AND INVARIANCE
    5. ARTIFICIAL INTELLIGENCE BASED METHODS
    6. CONCLUSION
    7. ACKNOWLEDGMENT
    8. REFERENCES
    9. KEY TERMS AND DEFINITIONS
  9. Chapter 4: Modeling and Language Support for the Pattern Management
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. ABSTRACT PATTERN TYPE
    5. SPECIFIC PATTERN TYPES
    6. MAPPING FORMULA
    7. IMPLEMENTATION OF THE ABSTRACT MODEL IN ORACLE
    8. SOLUTIONS AND RECOMMENDATIONS
    9. CONCLUSION
    10. REFERENCES
    11. KEY TERMS AND DEFINITIONS
  10. Chapter 5: Trading Orders Algorithm Development
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. THE PRINCIPLES OF TRADING ON THE STOCK, COMMODITY, AND CURRENCY MARKET
    5. STOCKS, COMMODITIES, CURRENCIES, CFDS, OPTIONS: DESCRIPTION OF INDIVIDUAL MARKETS
    6. FUNDAMENTAL AND TECHNICAL ANALYSIS
    7. INDICATORS OF TECHNICAL ANALYSIS
    8. THE USE OF INDICATORS IN TECHNICAL ANALYSIS
    9. DESCRIPTION OF THE USE OF SOFTWARE “METATRADER”
    10. DESIGN AND CONSTRUCTION OF ATS
    11. COMBINED TRADING SYSTEM
    12. EVALUATION APPROACH TO MANUAL VERSUS AUTOMATED TRADING SYSTEMS
    13. REFERENCES
    14. KEY TERMS AND DEFINITIONS
  11. Chapter 6: Analysis and Classification Tools for Automatic Process of Punches and Kicks Recognition
    1. ABSTRACT
    2. INTRODUCTION
    3. PUNCH AND KICK TECHNIQUES
    4. MEASURING STATION
    5. PARTICIPANTS OF THE EXPERIMENT
    6. ARTIFICIAL NEURAL NETWORKS
    7. EXPERIMENT DESIGN
    8. RESULTS
    9. CONCLUSION
    10. ACKNOWLEDGMENT
    11. REFERENCES
    12. KEY TERMS AND DEFINITIONS
  12. Chapter 7: Research on Processing the Brain Activity in BCI System
    1. ABSTRACT
    2. INTRODUCTION
    3. RESEARCH BACKGROUND
    4. RESEARCH ON BCI SYSTEM
    5. SOLUTIONS AND RECOMMENDATIONS
    6. FUTURE RESEARCH DIRECTIONS
    7. CONCLUSION
    8. ACKNOWLEDGMENT
    9. REFERENCES
    10. KEY TERMS AND DEFINITIONS
  13. Chapter 8: Distribution Signals between the Transmitter and Antenna – Event B Model
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. THE CHOICE OF AN ANTENNA FOR DVB-T RECEPTION
    5. DISTRIBUTION TV SIGNAL BETWEEN THE TRANSMITTER AND THE ANTENNA
    6. MODELLING THE PROPSAL RECEIVING SYSTEM DVB-T SIGNALS
    7. SOLUTION ABSTRACT MACHINE
    8. PARAMETER MEASURING OF CONSTELLATION DIAGRAMS IN PARTICULAR DVB-T SIGNALS
    9. FREQUENCY SPECTRUM OF DVB-T
    10. LEVELS OF ANALOG AND DVB-T SIGNALS
    11. MODULATION ERROR RATE (MER)
    12. AUSTRIA MUX
    13. LIST OF TRANSMITTERS AND CAPTURED INDIVIDUAL CHANNELS
    14. ABSTRACT MATHEMATICAL MODELLING
    15. CONCLUSION
    16. REFERENCES
    17. KEY TERM AND DEFINITIONS
  14. Related References
  15. Compilation of References
  16. About the Contributors