A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
PMP extends GNNs to handle past states efficiently.
Develops a more flexible HDP-HMM for temporal data segmentation.
A stochastic theory for the toppling activity in sandpile models is developed, based on a simple mean-field assumption about the toppling process. The theory describes the process as an anti-persistent Gaussian walk, where the diffusion coefficient is proportional to the activity. It is formulated as a generalization o…
New model clusters mixed-type data with missing values, improving air quality analysis.
Simpler ε-greedy with longer action durations improves exploration.
News sentiment in U.S. economic newspapers has become more persistent over 45 years.
Study uses topological signatures to quantify financial market complexity.
In multi-temporal SAR interferometry (MT-InSAR), persistent scatterer (PS) pixels are used to estimate geophysical parameters, essentially deformation. Conventionally, PS pixels are selected on the basis of the estimated noise present in the spatially uncorrelated phase component along with look-angle error in a tempor…
We study soft persistence (existence in subsequent temporal layers of motifs from the initial layer) of motif structures in Triangulated Maximally Filtered Graphs (TMFG) generated from time-varying Kendall correlation matrices computed from stock prices log-returns over rolling windows with exponential smoothing. We ob…
Simplicial persistence measures financial market dynamics, revealing long-term structure evolution.
Networks are a convenient way to represent complex systems of interacting entities. Many networks contain "communities" of nodes that are more densely connected to each other than to nodes in the rest of the network. In this paper, we investigate the detection of communities in temporal networks represented as multilay…
Algorithm estimates parameters over time-varying graphs without special assumptions.
The last decades have not only been characterized by an explosive growth of data, but also an increasing appreciation of data as a valuable resource. Their value comes with the ability to extract meaningful patterns that are of economic, societal or scientific relevance. A particular challenge is the identification of …
In many applications, observed data are influenced by some combination of latent causes. For example, suppose sensors are placed inside a building to record responses such as temperature, humidity, power consumption and noise levels. These random, observed responses are typically affected by many unobserved, latent fac…
Topological data analysis (TDA) has emerged as one of the most promising techniques to reconstruct the unknown shapes of high-dimensional spaces from observed data samples. TDA, thus, yields key shape descriptors in the form of persistent topological features that can be used for any supervised or unsupervised learning…
Study reveals investor heterogeneity in Korean equity market cash flows.
We explore the evolution of daily returns of four major US stock market indices during the technology crash of 2000, and the financial crisis of 2007-2009. Our methodology is based on topological data analysis (TDA). We use persistence homology to detect and quantify topological patterns that appear in multidimensional…
Study of 2D Ising model reveals patterns in financial markets.
Temporal aggregation reveals latent default correlation from monthly data.
Temporal coarse-graining of latent default paths explains effective correlation in corporate defaults.
Enhanced Zika spread forecasting using topological data analysis.
We study the temporal fluctuations in time-dependent stock prices (both individual and composite) as a stochastic phenomenon using general techniques and methods of nonequilibrium statistical mechanics. In particular, we analyze stock price fluctuations as a non-Markovian stochastic process using the first-passage stat…
We present an approach to model time series data from resting state fMRI for autism spectrum disorder (ASD) severity classification. We propose to adopt kernel machines and employ graph kernels that define a kernel dot product between two graphs. This enables us to take advantage of spatio-temporal information to captu…
Study finds long-range dependence in financial markets, but deep generative models struggle to replicate it.
Recurrent neural networks can be difficult to train on long sequence data due to the well-known vanishing gradient problem. Some architectures incorporate methods to reduce RNN state updates, therefore allowing the network to preserve memory over long temporal intervals. To address these problems of convergence, this p…
New method clusters infant vocalizations using topological data.
TMTF improves time series visualization by separating dynamic regimes.
Study shows different price correlations in European electricity markets.
SGDm with fixed step-size diverges under covariate shift, similar to a parametric oscillator.
Paper proposes BOCPD for real-time order flow and market impact prediction.
Approaches for approximating persistent homology for large datasets.
Persistence landscapes map persistence diagrams into a function space, which may often be taken to be a Banach space or even a Hilbert space. In the latter case, it is a feature map and there is an associated kernel. The main advantage of this summary is that it allows one to apply tools from statistics and machine lea…
We perform an analysis of fractal properties of the positive and the negative changes of the German DAX30 index separately using Multifractal Detrended Fluctuation Analysis (MFDFA). By calculating the singularity spectra we show that returns of both signs reveal multiscaling. Curiously, these spectra display a s…
Proposes deep graph persistence to address neural persistence issues in deep learning.
Paper proves -means clustering works on persistence diagrams.
Formula for interleaving distance of rectangle persistence modules.
This paper demonstrates the flaws of co-persistence theory proposed by Bollerslev and Engle (1993) which cause the theory can hardly be applied. With the introduction of the half-life of decay coefficient as the measure of the persistence, and both the weak definition of persistence and co-persistence in variance, this…
Optimizes wavelets for graph classification using spectral wavelet signatures and persistence diagrams.
New method for analyzing multiparameter persistence modules from smooth functions.
Develops robust persistence diagrams using kernel methods.
This paper interprets critical scales in persistent homology for compact metric spaces.
MuRiT efficiently computes multi-parameter persistence barcodes.
In this work we reduce undersampling artefacts in two-dimensional () golden-angle radial cine cardiac MRI by applying a modified version of the U-net. We train the network on spatio-temporal slices which are previously extracted from the image sequences. We compare our approach to two and a Deep Lear…
Persistent homology can recognize knotting in curves.
This article analyzes the relationship between co-persistence and hedging which indicates co-persistence ratio is just the long-term hedging ratio. The new method of exhaustive search algorithm for deriving co-persistence ratio is derived in the article. And we also develop a new hedging strategy of combining co-persis…
Despite the obvious similarities between the metrics used in topological data analysis and those of optimal transport, an optimal-transport based formalism to study persistence diagrams and similar topological descriptors has yet to come. In this article, by considering the space of persistence diagrams as a space of d…
Persistence diagrams are important descriptors in Topological Data Analysis. Due to the nonlinearity of the space of persistence diagrams equipped with their {\em diagram distances}, most of the recent attempts at using persistence diagrams in machine learning have been done through kernel methods, i.e., embeddings of …
Computational topology has recently known an important development toward data analysis, giving birth to the field of topological data analysis. Topological persistence, or persistent homology, appears as a fundamental tool in this field. In this paper, we study topological persistence in general metric spaces, with a …