Eigen Value Statistics of Long-Term Monthly Average Temperature of Meghalaya, India
Raju Kalita, Atul Saxena

TL;DR
This study applies Random Matrix Theory to analyze the eigenvalue spacing of temperature data from Meghalaya, revealing weak correlations among grid points through Brody distribution fitting.
Contribution
It introduces the use of RMT to analyze temperature eigenvalue statistics in Meghalaya, highlighting weak eigenvalue repulsion and correlation.
Findings
Eigenvalue spacing follows Brody distribution at β=0.045
Weak eigenvalue repulsion indicates low correlation among temperature grids
RMT effectively characterizes temperature data eigenvalue statistics
Abstract
We use Random Matrix Theory (RMT) to describe the eigenvalue spacing of Meghalaya's historical monthly average temperature () in grids. For that, the Nearest Neighbor Spacings () of the eigenvalues of the correlation matrices were found out for 1428 consecutive eigenvalue pair differences. It is found that the distribution of follows Brody distribution at a correlation value of . This value of indicates weak repulsion among the eigenvalues as it is closer to Poisson fluctuations, meaning there is a weak correlation among the grids.
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Taxonomy
TopicsComplex Systems and Time Series Analysis · Statistical Mechanics and Entropy · Financial Risk and Volatility Modeling
