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Updated: Jul 7, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Nonstationary time series analysis by temporal clustering.

S Policker¹, A B Geva

¹Dept. of Electr. & Comput. Eng., Ben-Gurion Univ. of the Negev, Beer-Sheva.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 5, 2008

Summary

This study introduces a new model and algorithms for analyzing nonstationary time series with changing regimes. It uses fuzzy clustering to estimate continuous drift, improving time series analysis.

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Area of Science:

Time Series Analysis
Machine Learning
Statistical Modeling

Background:

Nonstationary time series exhibit changing statistical properties over time.
Estimating parameters in time series with regime shifts is challenging.
Existing methods may not adequately capture continuous distributional drifts.

Purpose of the Study:

To present a novel model and algorithms for estimating parameters in nonstationary time series.
To apply fuzzy clustering for estimating continuous drift in time series distributions.
To develop a new cluster validity criterion for temporal patterns.

Main Methods:

Fuzzy clustering applied to time series data.
Interpretation of temporal membership matrix as weights in a time-varying mixture probability distribution function (PDF).
Analysis of algorithm stopping conditions to derive a novel cluster validity criterion.

Main Results:

Successful estimation of parameters for nonstationary time series with continuous regime change.
Demonstration of algorithm performance across three distinct signal types.
Development of a new validity criterion for fuzzy clustering of temporal data.

Conclusions:

The proposed model and algorithms effectively handle nonstationary time series with regime shifts.
Fuzzy clustering offers a robust approach for estimating continuous distributional drift.
The novel cluster validity criterion enhances the analysis of temporal patterns.