Data Mining In Time Series Databases 1st edition by Mark Last, Abraham Kandel, Horst Bunke ISBN 9812382909  978-9812382900

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ISBN 10: 9812382909
ISBN 13: 978-9812382900
Author: Mark Last, Abraham Kandel, Horst Bunke 

Adding the time dimension to real-world databases produces Time Series Databases (TSDB) and introduces new aspects and difficulties to data mining and knowledge discovery. This book covers the state-of-the-art methodology for mining time series databases. The novel data mining methods presented in the book include techniques for efficient segmentation, indexing, and classification of noisy and dynamic time series. A graph-based method for anomaly detection in time series is described and the book also studies the implications of a novel and potentially useful representation of time series as strings. The problem of detecting changes in data mining models that are induced from temporal databases is additionally discussed.

Data Mining In Time Series Databases 1st Table of contents:

Chapter 1: Introduction to Time Series Data and Data Mining
1.1 Overview of Time Series Data
1.2 Challenges of Mining Time Series Data
1.3 The Role of Data Mining in Time Series Analysis
1.4 Key Concepts in Data Mining and Time Series
1.5 Scope and Structure of the Book

Chapter 2: Fundamentals of Time Series Databases
2.1 What is a Time Series Database?
2.2 Characteristics of Time Series Data
2.3 Time Series Storage Models and Indexing Techniques
2.4 Temporal Data Models and Their Applications
2.5 Data Quality Issues in Time Series Databases

Chapter 3: Time Series Data Preprocessing
3.1 Data Cleaning and Noise Reduction
3.2 Time Series Normalization and Transformation
3.3 Missing Data Handling in Time Series
3.4 Smoothing Techniques
3.5 Seasonal Decomposition of Time Series
3.6 Feature Extraction and Selection for Time Series

Chapter 4: Clustering Time Series Data
4.1 Time Series Clustering: Concepts and Techniques
4.2 Distance Measures for Time Series
4.3 Partitioning and Hierarchical Clustering Methods
4.4 Model-Based Clustering Approaches
4.5 Dynamic Time Warping (DTW) for Time Series
4.6 Applications of Time Series Clustering

Chapter 5: Classification of Time Series Data
5.1 Overview of Time Series Classification
5.2 Supervised Learning Approaches for Time Series
5.3 Feature-Based Classification Methods
5.4 Model-Based Classification for Time Series
5.5 Time Series Classification with Deep Learning
5.6 Evaluation Metrics for Time Series Classification

Chapter 6: Anomaly Detection in Time Series
6.1 Introduction to Anomaly Detection
6.2 Techniques for Detecting Anomalies in Time Series
6.3 Statistical and Machine Learning Approaches
6.4 Seasonal and Trend-Based Anomaly Detection
6.5 Applications in Financial Markets, Healthcare, and IoT
6.6 Real-Time Anomaly Detection Systems

Chapter 7: Time Series Forecasting Methods
7.1 Fundamentals of Time Series Forecasting
7.2 Classical Time Series Models (ARIMA, Holt-Winters)
7.3 Machine Learning Approaches for Forecasting
7.4 Deep Learning Models for Time Series Forecasting
7.5 Hybrid Approaches to Forecasting
7.6 Evaluating Forecasting Accuracy and Error Metrics

Chapter 8: Mining Temporal Patterns and Trends
8.1 Temporal Pattern Mining Concepts
8.2 Frequent Pattern Mining in Time Series
8.3 Sequential Pattern Mining
8.4 Trend Analysis and Change Detection
8.5 Mining Periodicity and Seasonality in Time Series
8.6 Applications of Temporal Pattern Mining

Chapter 9: Time Series Mining in Big Data Environments
9.1 Challenges of Big Data for Time Series Mining
9.2 Scalable Algorithms for Large-Scale Time Series Data
9.3 Distributed Systems for Time Series Analysis
9.4 Cloud Computing and Time Series Databases
9.5 Real-Time Processing of Streaming Time Series Data

Chapter 10: Time Series Mining in Domain-Specific Applications
10.1 Financial Time Series Analysis
10.2 Time Series Mining in Healthcare and Bioinformatics
10.3 Sensor Data Analysis in IoT Systems
10.4 Environmental and Climate Data Mining
10.5 Time Series Applications in Manufacturing and Supply Chain
10.6 Case Studies of Time Series Data Mining in Industry

Chapter 11: Evaluation and Validation of Time Series Mining Models
11.1 Performance Metrics for Time Series Mining Models
11.2 Cross-Validation and Resampling Techniques
11.3 Model Interpretability and Explainability
11.4 Overfitting and Model Selection Strategies
11.5 Benchmarking Time Series Data Mining Algorithms

Chapter 12: Future Trends in Time Series Data Mining
12.1 Emerging Techniques in Time Series Mining
12.2 The Role of Artificial Intelligence and Deep Learning
12.3 Real-Time and Streaming Data Mining
12.4 Ethical and Privacy Concerns in Time Series Analysis
12.5 The Future of Time Series Databases and Mining Applications

References
Index

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