This paper proposes statistical procedures to identify coordinates responsible for change-points in multivariate time series data.

This paper proposes an online changepoint detection method for autoregressive processes of order p, improving detection power and computational efficiency for data with temporal correlation.

The paper introduces QuITE, a plug-and-play embedding module that uses learnable query tokens to effectively embed irregular multivariate time series data into latent representations compatible with e…

The paper proposes a formal verification framework for statistical programs in Python using Why3-py and extends StatWhy to verify meta-analysis methods.

The paper introduces the Hiremath Early Detection (HED) Score, a new measure-theoretic standard that accurately quantifies the time-value of early detection, significantly outperforming traditional me…

The paper proposes methodologies to measure lag relevance in machine learning forecasting models using Ghost variables, Shapley values, and additive importance measures. It also introduces auto-releva…

The paper introduces Gradient-based Behavior Vector-Parameter Delta (GBV-PD), an approach for evolution-aware regression test prioritization in ML-enabled systems using gradient-based behavior vectors…

This paper introduces EVOTS, an evolutionary neural architecture search framework for discovering task-adaptive Transformer-like models for multivariate time-series forecasting.

The paper proposes a novel nonparametric mutual information estimator to robustly quantify dependence between heterogeneous temporal data, specifically continuous time series and discrete event sequen…

ChronosAD introduces a novel architecture that uses time series foundation models and a custom Temporal Block to achieve robust and highly accurate anomaly detection across diverse domains.

This paper presents NL2Test, a tool that generates executable API regression tests from natural-language scenario descriptions and traffic captures, achieving an 82.4% exact-match rate in industrial s…

This paper establishes the identifiability of latent regimes and regime-dependent causal structures in complex non-stationary time series modeled by Markov Switching Models, even with instantaneous ef…

The paper introduces a modular version of rank and linear-complexity tests for pseudorandom number generators and provides a Rust program, modlin, to detect statistical bias in generators that are lin…

This paper proposes Stringology-Based Cryptology (SBC), a novel approach that analyzes the structural properties of cryptographic outputs by treating them as symbolic sequences, offering complementary…

This paper evaluates the improvement of statistical fault localization by augmenting it with execution features.

This paper proposes the RFFBCGA algorithm, a random Fourier feature based bias-compensated filter that mitigates input noise interference and enhances robustness in nonlinear adaptive filtering.

The paper introduces a stringology-based fingerprinting (SBF) framework to structurally analyze cryptographic sequences, demonstrating that pattern analysis can reveal measurable structural signatures…