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Cluster Sampling Method

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Transductive Feature Selection Using Clustering-Based Sample Entropy for Temperature Prediction in Weather

Zahra Karevan¹, Johan A K Suykens¹

¹ESAT-STADIUS (Department of Electrical Engineering-Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics), KU Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium.

Entropy (Basel, Switzerland)

|December 3, 2020

Summary

This summary is machine-generated.

This study introduces clustering-based sample entropy to improve time series analysis. The novel method enhances feature selection for weather forecasting by considering data seasonality and local structures.

Keywords:

conditional entropy feature selection information transfer transductive learning weather forecasting

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Area of Science:

Information theory
Time series analysis
Dynamical systems

Background:

Entropy measures quantify information content in dynamical systems.
Sample entropy is a model-free method for time series information transfer.
Identifying informative past values in conditional entropy is crucial.

Purpose of the Study:

To develop a lag-specific conditional entropy to identify informative past values.
To propose a clustering-based sample entropy method that exploits temporal information and seasonality.
To apply this method for transductive feature selection in black-box weather forecasting.

Main Methods:

Utilized lag-specific conditional entropy to pinpoint relevant past data points.
Introduced clustering-based sample entropy, incorporating training data clusters and test point membership.
Applied the method to predict minimum and maximum temperatures in Brussels for 1-6 days ahead.

Main Results:

The clustering-based approach improved feature selection performance by considering local data structures.
Competitive forecasting accuracy was achieved even with a significant reduction in the number of features.
The method demonstrated effectiveness in transductive feature selection for black-box models.

Conclusions:

Considering local data structures and seasonality via clustering-based sample entropy enhances feature selection.
This approach offers a robust method for time series analysis and forecasting.
The proposed technique provides efficient feature selection without compromising predictive performance.