CHEONG Siew Ann

Research topics:

• Computational physics
• Complex system dynamics
• Bioinformatics

For more information, visit webpage

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311 to 320 of 771 Results

QDwsgnheatmap.py Sep 23, 2024 - Quasi-Differentiation and Its Applications to Noisy Time Series Data from Complex Systems Python Source Code - 785 B - MD5: 56911b796922a114dd8d9754a26f7bc3 Python script to compute and plot the heat map of the quasi-derivative of a square pulse for different window sizes.
QDwsgn.py Sep 23, 2024 - Quasi-Differentiation and Its Applications to Noisy Time Series Data from Complex Systems Python Source Code - 1.3 KB - MD5: 6eeafee0af043aa3ea5fda26019fd91c Python script to compute and plot the quasi-derivative of a square pulse for different window sizes.
Indicator from the graph Laplacian of stock market time series cross sections can precisely determine the durations of market crashes Sep 23, 2024 - MOE AcRF Tier 2 grant MOE-000442-00 Kang, Zheng Tien, 2024, "Indicator from the graph Laplacian of stock market time series cross sections can precisely determine the durations of market crashes", https://doi.org/10.21979/N9/7YNZAQ, DR-NTU (Data), V2 This repository include the processed ultrametric distance matrices data, MATLAB scripts and data holder files (in .mat format) used to generate the results and figures in the PLOS paper with the above title.
Codes.7z Sep 23, 2024 - Indicator from the graph Laplacian of stock market time series cross sections can precisely determine the durations of market crashes Compressed Archive - 320.3 KB - MD5: 85a788dc793aa70da2988f0608c98d84 MATLAB scripts and data holder files The ultrametric distance matrices (stored in .dat files, which are archived and zipped in .7z format) need to be fed into MATLAB via the MATLAB scripts in this archive to produce the .mat files (also in this zipped archive) for further analys...
Data (N225, SGX, SP500).7z Sep 23, 2024 - Indicator from the graph Laplacian of stock market time series cross sections can precisely determine the durations of market crashes Compressed Archive - 3.8 GB - MD5: 61211c31ae7a5fa3b9bc0622ab79b3a4 Ultrametric distance matrices for S&P 500, Nikkei 225 and SGX (whole market) The .dat ultrametric distance files are processed from Yahoo Finance stock price return's cross-correlation data. The files are grouped by markets (S&P 500, Nikkei 225 and the whole SGX market), by time...
Data (TWSE).7z Sep 23, 2024 - Indicator from the graph Laplacian of stock market time series cross sections can precisely determine the durations of market crashes Compressed Archive - 6.8 GB - MD5: bcf13c5ba12eb1afa1361caa750ce777 Ultrametric distance matrices for TWSE (whole market) The .dat ultrametric distance files are processed from Yahoo Finance stock price return's cross-correlation data. The TAIEX index (tracking the entire TWSE market) has about 900-1000 stocks, twice the number of constituents o...
README.txt Sep 23, 2024 - Indicator from the graph Laplacian of stock market time series cross sections can precisely determine the durations of market crashes Plain Text - 4.7 KB - MD5: dd06d65f5edaa472e3c36040bd5384fc Documentation Detailed dataset description for reader's reference.
Data-Driven Approach to Understanding Complex Urban Phe-nomena: A Preliminary Study on the Gentrification of H Street NE in Washington DC Jul 9, 2024 Cheong, Siew Ann, 2024, "Data-Driven Approach to Understanding Complex Urban Phe-nomena: A Preliminary Study on the Gentrification of H Street NE in Washington DC", https://doi.org/10.21979/N9/N9F5ZU, DR-NTU (Data), V1 Python scripts and secondary data sets used in our study of the gentrification of H Street NE in Washington DC.
Dates.npy Jul 9, 2024 - Data-Driven Approach to Understanding Complex Urban Phe-nomena: A Preliminary Study on the Gentrification of H Street NE in Washington DC Unknown - 21.7 KB - MD5: 823b43c6b5f8b6dab78b29c139574d38
FGStNE.npy Jul 9, 2024 - Data-Driven Approach to Understanding Complex Urban Phe-nomena: A Preliminary Study on the Gentrification of H Street NE in Washington DC Unknown - 1.9 KB - MD5: 2ebf4e4dd8e94ecd5f7e81fb19df7b08

QDwsgnheatmap.py

Sep 23, 2024 - Quasi-Differentiation and Its Applications to Noisy Time Series Data from Complex Systems

Python Source Code - 785 B -

Python script to compute and plot the heat map of the quasi-derivative of a square pulse for different window sizes.

QDwsgn.py

Sep 23, 2024 - Quasi-Differentiation and Its Applications to Noisy Time Series Data from Complex Systems

Python Source Code - 1.3 KB -

Python script to compute and plot the quasi-derivative of a square pulse for different window sizes.

Indicator from the graph Laplacian of stock market time series cross sections can precisely determine the durations of market crashes

Sep 23, 2024 - MOE AcRF Tier 2 grant MOE-000442-00

Kang, Zheng Tien, 2024, "Indicator from the graph Laplacian of stock market time series cross sections can precisely determine the durations of market crashes", https://doi.org/10.21979/N9/7YNZAQ, DR-NTU (Data), V2

This repository include the processed ultrametric distance matrices data, MATLAB scripts and data holder files (in .mat format) used to generate the results and figures in the PLOS paper with the above title.

Codes.7z

Sep 23, 2024 - Indicator from the graph Laplacian of stock market time series cross sections can precisely determine the durations of market crashes

Compressed Archive - 320.3 KB -

MATLAB scripts and data holder files The ultrametric distance matrices (stored in .dat files, which are archived and zipped in .7z format) need to be fed into MATLAB via the MATLAB scripts in this archive to produce the .mat files (also in this zipped archive) for further analys...

Data (N225, SGX, SP500).7z

Sep 23, 2024 - Indicator from the graph Laplacian of stock market time series cross sections can precisely determine the durations of market crashes

Compressed Archive - 3.8 GB -

Ultrametric distance matrices for S&P 500, Nikkei 225 and SGX (whole market) The .dat ultrametric distance files are processed from Yahoo Finance stock price return's cross-correlation data. The files are grouped by markets (S&P 500, Nikkei 225 and the whole SGX market), by time...

Data (TWSE).7z

Sep 23, 2024 - Indicator from the graph Laplacian of stock market time series cross sections can precisely determine the durations of market crashes

Compressed Archive - 6.8 GB -

Ultrametric distance matrices for TWSE (whole market) The .dat ultrametric distance files are processed from Yahoo Finance stock price return's cross-correlation data. The TAIEX index (tracking the entire TWSE market) has about 900-1000 stocks, twice the number of constituents o...

README.txt

Sep 23, 2024 - Indicator from the graph Laplacian of stock market time series cross sections can precisely determine the durations of market crashes

Plain Text - 4.7 KB -

Documentation

Detailed dataset description for reader's reference.

Data-Driven Approach to Understanding Complex Urban Phe-nomena: A Preliminary Study on the Gentrification of H Street NE in Washington DC

Jul 9, 2024

Cheong, Siew Ann, 2024, "Data-Driven Approach to Understanding Complex Urban Phe-nomena: A Preliminary Study on the Gentrification of H Street NE in Washington DC", https://doi.org/10.21979/N9/N9F5ZU, DR-NTU (Data), V1

Python scripts and secondary data sets used in our study of the gentrification of H Street NE in Washington DC.

Dates.npy

Jul 9, 2024 - Data-Driven Approach to Understanding Complex Urban Phe-nomena: A Preliminary Study on the Gentrification of H Street NE in Washington DC

Unknown - 21.7 KB -

FGStNE.npy

Jul 9, 2024 - Data-Driven Approach to Understanding Complex Urban Phe-nomena: A Preliminary Study on the Gentrification of H Street NE in Washington DC

Unknown - 1.9 KB -

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