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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Jump point detection using empirical mode decomposition.

Benson S Y Lam¹, Carisa K W Yu¹, Siu-Kai Choy¹

¹Department of Mathematics and Statistics, Hang Seng Management College, Hang Shin Link, Siu Lek Yuen, Shatin, Hong Kong.

Land Use Policy

|April 15, 2020

Summary

This summary is machine-generated.

Detecting jump points in Hong Kong housing prices is crucial for economic insights. This study introduces a new method using empirical mode decomposition and derivative detection, outperforming existing techniques for real estate market analysis.

Keywords:

Empirical mode decomposition Jump points Real estate Time series

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Area of Science:

* Economics
* Financial Mathematics
* Data Science

Background:

* Real estate is a significant investment in Hong Kong.
* Jump points in housing price time series offer vital economic insights.
* Accurate detection of these jumps aids policymakers and investors.

Purpose of the Study:

* To propose a novel methodology for detecting jump points in Hong Kong housing price time series.
* To enhance the analysis of the real estate market through improved jump point detection.
* To provide a more effective tool for economic forecasting and investment strategies.

Main Methods:

* Empirical Mode Decomposition (EMD) algorithm.
* Derivative-based detection technique.
* Application to housing price indices time series in Hong Kong.

Main Results:

* The proposed method successfully detected jump points in housing price data.
* Experimental results demonstrated superior performance compared to existing methods.
* The EMD and derivative-based approach outperformed the wavelet-based approach.

Conclusions:

* The developed jump point detection methodology is effective for Hong Kong's real estate market.
* This approach offers a significant improvement over current state-of-the-art techniques.
* Findings support better economic policy and investment decisions in real estate.