Feature identification in time series data sets.

Area of Science:

• Geophysics
• Time Series Analysis
• Environmental Science

Background:

• Geophysical phenomena often affect multiple variables concurrently.
• Identifying these simultaneous perturbations in time series data is crucial for understanding physical processes.
• Existing methods may lack flexibility or computational efficiency.

Purpose of the Study:

• To present a computationally inexpensive and flexible feature identification method for geophysical data.
• To enable automated detection of significant events in multi-variable time series.
• To allow analysts to incorporate physical context for method tuning.

Main Methods:

• Compares multiple time series to identify features.
• Defines features as time periods with local maxima of absolute deviation across all series.
• Allows user-defined parameter tuning based on physical knowledge.

Main Results:

• Successfully applied to datasets from Monterey Bay and Yax Chen Cave System.
• Demonstrates automated identification of geophysically relevant features.
• Highlights the method's flexibility and computational efficiency.

Conclusions:

• The developed method offers an effective approach for feature identification in geophysical time series.
• It facilitates the automated discovery of events warranting further investigation.
• The tunable nature enhances its applicability across diverse geophysical settings.