Dynamic scaling approach to study time series fluctuations.

Area of Science:

• Statistical physics
• Time series analysis
• Complex systems

Background:

• Stochastic time series analysis is crucial in many scientific fields.
• Existing methods may not fully capture the complex dynamics of real-world fluctuations.

Purpose of the Study:

• To develop a novel framework for analyzing stochastic time series.
• To leverage kinetic roughening theory for improved time series modeling and forecasting.

Main Methods:

• Mapping time series fluctuation dynamics to a nonequilibrium surface-growth problem.
• Utilizing fluctuation sampling interval as the time variable and physical time as the spatial variable.
• Applying the Family-Viscek dynamic scaling ansatz.

Main Results:

• Demonstrated that fluctuations in many real-world time series adhere to the Family-Viscek dynamic scaling analog.
• Established a connection between time series analysis and surface-growth phenomena.

Conclusions:

• The proposed approach offers a powerful new perspective for understanding and predicting time series behavior.
• Kinetic roughening theory provides effective tools for classifying, modeling, and forecasting stochastic time series fluctuations.