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Sushovan Majhi

Publications and source records attributed to Sushovan Majhi.

34 records · Page 2Linked to original sources
arXiv2025

Topology of The Polar Vortex and Montana Weather

This paper explores the use of Topological Data Analysis (TDA) to investigate patterns in zonal-mean zonal winds of the Arctic, which make up the polar vortex, in order to better explain polar vortex dynamics. We demonstrate how TDA reveals significant topological features in this polar vortex data, and how they may relate these features to the collapse of the stratospheric vortex during the winter in the northern hemisphere. Using a time series representation of this data, we build a point cloud using the principles of Takens' Embedding theorem and apply persistent homology to uncover nontrivial topological structures that provide insight into the dynamical system's chaotic and periodic behaviors. These structures can offer new perspectives on the dynamics of the polar vortex, and perhaps other weather regimes, all of which have a global impact. Our results show clear transitions between seasons, with substantial increases in topological activity during periods of extreme cold. This is particularly evident in the historically strong polar vortex event of early 2016. Our analysis captures the persistence of topological features during such events and may even offer insights into vortex splitting, as indicated by the number of distinct persistent features. This work highlights the potential of TDA in climate science, offering a novel approach to studying complex dynamical systems.

math.DS↗
arXiv2025

Detecting the Indian Monsoon using Topological Data Analysis

A monsoon is a wind system that seasonally reverses its direction, accompanied by corresponding changes in precipitation. The Indian monsoon is the most prominent monsoon system, primarily affecting India's rainy season and its surrounding lands and water bodies. Every year, the onset and withdrawal of this monsoon happens sometime in May-June and September-October, respectively. Since monsoons are very complex systems governed by various weather factors with random noise, the yearly variability in the dates is significant. Despite the best efforts by the India Meteorological Department (IMD) and the South Asia Climate Outlook Forum (SCOF), forecasting the exact dates of onset and withdrawal, even within a week, is still an elusive problem in climate science. We interpret the onset and withdrawal of the Indian monsoon as abrupt regime shifts into and out of chaos. During these transitions, topological signatures (e.g., persistence diagrams) show rapid fluctuations, indicative of chaotic behavior. To detect these shifts, we reconstruct the phase space using Takens' embedding of the Indian monsoon index and apply topological data analysis (TDA) to track the birth and death of $k$-dimensional features. Applying this approach to historical monsoon index data (1948-2015) suggests a promising framework for more accurate detection of monsoon onset and withdrawal.

physics.ao-ph↗
arXiv2025

Causality Analysis of COVID-19 Induced Crashes in Stock and Commodity Markets: A Topological Perspective

The paper presents a comprehensive causality analysis of the US stock and commodity markets during the COVID-19 crash. The dynamics of different sectors are also compared. We use Topological Data Analysis (TDA) on multidimensional time-series to identify crashes in stock and commodity markets. The Wasserstein Distance WD shows distinct spikes signaling the crash for both stock and commodity markets. We then compare the persistence diagrams of stock and commodity markets using the WD metric. A significant spike in the $WD$ between stock and commodity markets is observed during the crisis, suggesting significant topological differences between the markets. Similar spikes are observed between the sectors of the US market as well. Spikes obtained may be due to either a difference in the magnitude of crashes in the two markets (or sectors), or from the temporal lag between the two markets suggesting information flow. We study the Granger-causality between stock and commodity markets and also between different sectors. The results show a bidirectional Granger-causality between commodity and stock during the crash period, demonstrating the greater interdependence of financial markets during the crash. However, the overall analysis shows that the causal direction is from stock to commodity. A pairwise Granger-causal analysis between US sectors is also conducted. There is a significant increase in the interdependence between the sectors during the crash period. TDA combined with Granger-causality effectively analyzes the interdependence and sensitivity of different markets and sectors.

q-fin.ST↗
arXiv2025

Hausdorff vs Gromov-Hausdorff distances

Let $M$ be a closed Riemannian manifold and let $X\subseteq M$. If the sample $X$ is sufficiently dense relative to the curvature of $M$, then the Gromov-Hausdorff distance between $X$ and $M$ is bounded from below by half their Hausdorff distance, namely $d_{GH}(X,M) \ge \frac{1}{2} d_H(X,M)$. The constant $\frac{1}{2}$ can be improved depending on the dimension and curvature of the manifold $M$, and obtains the optimal value $1$ in the case of the unit circle, meaning that if $X\subseteq S^1$ satisfies $d_{GH}(X,S^1)<\tfracπ{6}$, then $d_{GH}(X,S^1)=d_H(X,S^1)$. We also provide versions lower bounding the Gromov-Hausdorff distance $d_{GH}(X,Y)$ between two subsets $X,Y\subseteq M$. Our proofs convert discontinuous functions between metric spaces into simplicial maps between Čech or Vietoris-Rips complexes. We then produce topological obstructions to the existence of certain maps using the nerve lemma and the fundamental class of the manifold, thus lower bounding the Gromov-Hausdorff distance.

math.MG↗
arXiv2024

Identifying Extreme Events in the Stock Market: A Topological Data Analysis

This paper employs Topological Data Analysis (TDA) to detect extreme events (EEs) in the stock market at a continental level. Previous approaches, which analyzed stock indices separately, could not detect EEs for multiple time series in one go. TDA provides a robust framework for such analysis and identifies the EEs during the crashes for different indices. The TDA analysis shows that $L^1$, $L^2$ norms and Wasserstein distance ($W_D$) of the world leading indices rise abruptly during the crashes, surpassing a threshold of $μ+4*σ$ where $μ$ and $σ$ are the mean and the standard deviation of norm or $W_D$, respectively. Our study identified the stock index crashes of the 2008 financial crisis and the COVID-19 pandemic across continents as EEs. Given that different sectors in an index behave differently, a sector-wise analysis was conducted during the COVID-19 pandemic for the Indian stock market. The sector-wise results show that after the occurrence of EE, we have observed strong crashes surpassing $μ+2*σ$ for an extended period for the banking sector. While for the pharmaceutical sector, no significant spikes were noted. Hence, TDA also proves successful in identifying the duration of shocks after the occurrence of EEs. This also indicates that the Banking sector continued to face stress and remained volatile even after the crash. This study gives us the applicability of TDA as a powerful analytical tool to study EEs in various fields.

q-fin.ST↗
arXiv2024

Complex network analysis of cryptocurrency market during crashes

This paper identifies the cryptocurrency market crashes and analyses its dynamics using the complex network. We identify three distinct crashes during 2017-20, and the analysis is carried out by dividing the time series into pre-crash, crash, and post-crash periods. Partial correlation based complex network analysis is carried out to study the crashes. Degree density ($ρ_D$), average path length ($\bar{l}$), and average clustering coefficient ($\overline{cc}$) are estimated from these networks. We find that both $ρ_D$ and $\overline{cc}$ are smallest during the pre-crash period, and spike during the crash suggesting the network is dense during a crash. Although $ρ_D$ and $\overline{cc}$ decrease in the post-crash period, they remain higher than pre-crash levels for the 2017-18 and 2018-19 crashes suggesting a market attempt to return to normalcy. We get $\bar{l}$ is minimal during the crash period, suggesting a rapid flow of information. A dense network and rapid information flow suggest that during a crash uninformed synchronized panic sell-off happens. However, during the 2019-20 crash, the values of $ρ_D$, $\overline{cc}$, and $\bar{l}$ did not vary significantly, indicating minimal change in dynamics compared to other crashes. The findings of this study may guide investors in making decisions during market crashes.

q-fin.ST↗
arXiv2024

Demystifying Latschev's Theorem: Manifold Reconstruction from Noisy Data

For a closed Riemannian manifold $\mathcal{M}$ and a metric space $S$ with a small Gromov$\unicode{x2013}$Hausdorff distance to it, Latschev's theorem guarantees the existence of a sufficiently small scale $β>0$ at which the Vietoris$\unicode{x2013}$Rips complex of $S$ is homotopy equivalent to $\mathcal{M}$. Despite being regarded as a stepping stone to the topological reconstruction of Riemannian manifolds from a noisy data, the result is only a qualitative guarantee. Until now, it had been elusive how to quantitatively choose such a proximity scale $β$ in order to provide sampling conditions for $S$ to be homotopy equivalent to $\mathcal{M}$. In this paper, we prove a stronger and pragmatic version of Latschev's theorem, facilitating a simple description of $β$ using the sectional curvatures and convexity radius of $\mathcal{M}$ as the sampling parameters. Our study also delves into the topological recovery of a closed Euclidean submanifold from the Vietoris$\unicode{x2013}$Rips complexes of a Hausdorff close Euclidean subset. As already known for Čech complexes, we show that Vietoris$\unicode{x2013}$Rips complexes also provide topologically faithful reconstruction guarantees for submanifolds.

math.AT↗
arXiv2023

Metric and Path-Connectedness Properties of the Frechet Distance for Paths and Graphs

The Frechet distance is often used to measure distances between paths, with applications in areas ranging from map matching to GPS trajectory analysis to handwriting recognition. More recently, the Frechet distance has been generalized to a distance between two copies of the same graph embedded or immersed in a metric space; this more general setting opens up a wide range of more complex applications in graph analysis. In this paper, we initiate a study of some of the fundamental topological properties of spaces of paths and of graphs mapped to R^n under the Frechet distance, in an effort to lay the theoretical groundwork for understanding how these distances can be used in practice. In particular, we prove whether or not these spaces, and the metric balls therein, are path-connected.

cs.CG↗
arXiv2023

Graph Mover's Distance: An Efficiently Computable Distance Measure for Geometric Graphs

Many applications in pattern recognition represent patterns as a geometric graph. The geometric graph distance (GGD) has recently been studied as a meaningful measure of similarity between two geometric graphs. Since computing the GGD is known to be $\mathcal{NP}$-hard, the distance measure proves an impractical choice for applications. As a computationally tractable alternative, we propose in this paper the Graph Mover's Distance (GMD), which has been formulated as an instance of the earth mover's distance. The computation of the GMD between two geometric graphs with at most $n$ vertices takes only $O(n^3)$-time. Alongside studying the metric properties of the GMD, we investigate the stability of the GGD and GMD. The GMD also demonstrates extremely promising empirical evidence at recognizing letter drawings from the {\tt LETTER} dataset \cite{da_vitoria_lobo_iam_2008}.

cs.CG↗
arXiv2023

Vietoris$\unicode{x2013}$Rips Complexes of Metric Spaces Near a Metric Graph

For a sufficiently small scale $β>0$, the Vietoris$\unicode{x2013}$Rips complex $\mathcal{R}_β(S)$ of a metric space $S$ with a small Gromov$\unicode{x2013}$Hausdorff distance to a closed Riemannian manifold $M$ has been already known to recover $M$ up to homotopy type. While the qualitative result is remarkable and generalizes naturally to the recovery of spaces beyond Riemannian manifolds$\unicode{x2014}$such as geodesic metric spaces with a positive convexity radius$\unicode{x2014}$the generality comes at a cost. Although the scale parameter $β$ is known to depend only on the geometric properties of the geodesic space, how to quantitatively choose such a $β$ for a given geodesic space is still elusive. In this work, we focus on the topological recovery of a special type of geodesic space, called a metric graph. For an abstract metric graph $\mathcal{G}$ and a (sample) metric space $S$ with a small Gromov$\unicode{x2013}$Hausdorff distance to it, we provide a description of $β$ based on the convexity radius of $\mathcal{G}$ in order for $\mathcal{R}_β(S)$ to be homotopy equivalent to $\mathcal{G}$. Our investigation also extends to the study of the Vietoris$\unicode{x2013}$Rips complexes of a Euclidean subset $S\subset\mathbb{R}^d$ with a small Hausdorff distance to an embedded metric graph $\mathcal{G}\subset\mathbb{R}^d$. From the pairwise Euclidean distances of points of $S$, we introduce a family (parametrized by $\varepsilon$) of path-based Vietoris$\unicode{x2013}$Rips complexes $\mathcal{R}^\varepsilon_β(S)$ for a scale $β>0$. Based on the convexity radius and distortion of the embedding of $\mathcal{G}$, we show how to choose a suitable parameter $\varepsilon$ and a scale $β$ such that $\mathcal{R}^\varepsilon_β(S)$ is homotopy equivalent to $\mathcal{G}$.

math.AT↗
arXiv2022

Distance Measures for Geometric Graphs

A geometric graph is a combinatorial graph, endowed with a geometry that is inherited from its embedding in a Euclidean space. Formulation of a meaningful measure of (dis-)similarity in both the combinatorial and geometric structures of two such geometric graphs is a challenging problem in pattern recognition. We study two notions of distance measures for geometric graphs, called the geometric edit distance (GED) and geometric graph distance (GGD). While the former is based on the idea of editing one graph to transform it into the other graph, the latter is inspired by inexact matching of the graphs. For decades, both notions have been lending themselves well as measures of similarity between attributed graphs. If used without any modification, however, they fail to provide a meaningful distance measure for geometric graphs -- even cease to be a metric. We have curated their associated cost functions for the context of geometric graphs. Alongside studying the metric properties of GED and GGD, we investigate how the two notions compare. We further our understanding of the computational aspects of GGD by showing that the distance is $\mathcal{NP}$-hard to compute, even if the graphs are planar and arbitrary cost coefficients are allowed.

cs.CG↗
arXiv2022

On the Reconstruction of Geodesic Subspaces of $\mathbb{R}^N$

We consider the topological and geometric reconstruction of a geodesic subspace of $\mathbb{R}^N$ both from the Čech and Vietoris-Rips filtrations on a finite, Hausdorff-close, Euclidean sample. Our reconstruction technique leverages the intrinsic length metric induced by the geodesics on the subspace. We consider the distortion and convexity radius as our sampling parameters for a successful reconstruction. For a geodesic subspace with finite distortion and positive convexity radius, we guarantee a correct computation of its homotopy and homology groups from the sample. For geodesic subspaces of $\mathbb{R}^2$, we also devise an algorithm to output a homotopy equivalent geometric complex that has a very small Hausdorff distance to the unknown shape of interest.

math.AT↗
arXiv2022

Approximating Gromov-Hausdorff Distance in Euclidean Space

The Gromov-Hausdorff distance $(d_{GH})$ proves to be a useful distance measure between shapes. In order to approximate $d_{GH}$ for compact subsets $X,Y\subset\mathbb{R}^d$, we look into its relationship with $d_{H,iso}$, the infimum Hausdorff distance under Euclidean isometries. As already known for dimension $d\geq 2$, the $d_{H,iso}$ cannot be bounded above by a constant factor times $d_{GH}$. For $d=1$, however, we prove that $d_{H,iso}\leq\frac{5}{4}d_{GH}$. We also show that the bound is tight. In effect, this gives rise to an $O(n\log{n})$-time algorithm to approximate $d_{GH}$ with an approximation factor of $\left(1+\frac{1}{4}\right)$.

math.MG↗
arXiv2021

A sentiment-based modeling and analysis of stock price during the COVID-19: U- and Swoosh-shaped recovery

Recently, a stock price model is proposed by A. Mahata et al. [Physica A, 574, 126008 (2021)] to understand the effect of COVID-19 on stock market. It describes V- and L-shaped recovery of the stocks and indices, but fails to simulate the U- and Swoosh-shaped recovery that arises due to sharp crisis and prolong drop followed by quick recovery (U-shaped) or slow recovery for longer period (Swoosh-shaped recovery). We propose a modified model by introducing a new variable $θ$ that quantifies the sentiment of the investors. $θ=+1,~0,~-1$ for positive, neutral and negative sentiment, respectively. The model explains the movement of sectoral indices with positive $ϕ$ showing U- and Swoosh-shaped recovery. The simulation using synthetic fund-flow ($Ψ_{st}$) with different shock lengths ($T_S$), $ϕ$, negative sentiment period ($T_N$) and portion of fund-flow ($λ$) during recovery period show U- and Swoosh-shaped recovery. The results show that the recovery of the indices with positive $ϕ$ becomes very weak with the extended $T_S$ and $T_N$. The stocks with higher $ϕ$ and $λ$ recover quickly. The simulation of the Nifty Bank, Nifty Financial and Nifty Realty show U-shaped recovery and Nifty IT shows Swoosh-shaped recovery. The simulation result is consistent with the real stock price movement. The time-scale ($τ$) of the shock and recovery of these indices during the COVID-19 are consistent with the time duration of the change of negative sentiment from the onset of the COVID-19. This study may help the investors to plan their investment during different crises.

q-fin.ST↗
arXiv2019

Threshold-Based Graph Reconstruction Using Discrete Morse Theory

Discrete Morse theory has recently been applied in metric graph reconstruction from a given density function concentrated around an (unknown) underlying embedded graph. We propose a new noise model for the density function to reconstruct a connected graph both topologically and geometrically.

cs.CG↗