Time series trend detection and forecasting using complex network topology analysis.

Area of Science:

• Data Mining
• Machine Learning
• Complex Systems Analysis

Background:

• Extracting knowledge from time series data is crucial for real-world applications.
• Stochastic time series present significant challenges for traditional analysis methods.
• Advancements in data mining and machine learning offer new approaches to time series analysis.

Purpose of the Study:

• To develop a novel algorithm for trend detection in stochastic time series.
• To explore the application of complex network theory and topological features for time series analysis.
• To improve the predictability of time series by analyzing their network representations.

Main Methods:

• Generation of complex networks from time series data.
• Application of community detection algorithms on the generated networks.
• Utilizing network metrics to identify topological features for trend analysis.
• Development of a trend detection algorithm based on these network properties.

Main Results:

• The proposed algorithm effectively detects trends in stochastic time series.
• The method demonstrates advantages over traditional techniques, including adaptive class numbers and better noise absorption.
• Time series trends are represented by communities in topological space, offering a new perspective.
• Experimental results on artificial and real datasets show successful classification of local and global patterns.

Conclusions:

• The novel approach using complex networks and topological features provides a new paradigm for time series analysis.
• This method enhances the predictability of time series by capturing underlying patterns.
• The algorithm offers a robust and adaptive solution for trend detection in complex, stochastic data.