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.
Automated feature extraction for bearing health monitoring.
The paper improves safety in autonomous systems using adversarial learning.
On-line estimation plays an important role in process control and monitoring. Obtaining a theoretical solution to the simultaneous state-parameter estimation problem for non-linear stochastic systems involves solving complex multi-dimensional integrals that are not amenable to analytical solution. While basic sequentia…
Paper quantifies uncertainties in EIS spectra of SOFCs, proposing VB method for online monitoring.
The study evaluates three methods for real-time anomaly detection in CNC turning processes.
New scheme detects anomalies in time series data quickly and accurately.
Bayesian On-line Changepoint Detection is extended to on-line model selection and non-stationary spatio-temporal processes. We propose spatially structured Vector Autoregressions (VARs) for modelling the process between changepoints (CPs) and give an upper bound on the approximation error of such models. The resulting …
In this paper, we introduce a matrix-valued time series model for foreign exchange market. We then formulate trading matrices, foreign exchange options and return options (matrices), as well as on-line portfolio strategies. Moreover, we attempt to predict returns of portfolios by developing a cross rate method. This le…
Framework detects anomalies in fleet-based machine monitoring.
Introduces relative stability conditions on triangulated categories.
Research compares ML and Time Series methods for generating trading signals.
We consider on-line density estimation with a parameterized density from the exponential family. The on-line algorithm receives one example at a time and maintains a parameter that is essentially an average of the past examples. After receiving an example the algorithm incurs a loss which is the negative log-likelihood…
Study monitors wind turbine drivetrain bearings using dictionary learning from vibration data.
In this paper we propose a computationally efficient algorithm for on-line variable selection in multivariate regression problems involving high dimensional data streams. The algorithm recursively extracts all the latent factors of a partial least squares solution and selects the most important variables for each facto…
In this paper, we establish a robustification of an on-line algorithm for modelling asset prices within a hidden Markov model (HMM). In this HMM framework, parameters of the model are guided by a Markov chain in discrete time, parameters of the asset returns are therefore able to switch between different regimes. The p…
Paper discusses ASD challenge for machine condition monitoring.
Paper improves ETF tail-risk monitoring reliability.
We briefly review our recent studies on stochastic processes modelling internet on-line trading. We present a way to evaluate the average waiting time between the observation of the price in financial markets and the next price change, especially in an on-line foreign exchange trading service for individual customers v…
New monitoring method detects ML risk models' performance changes in medical interventions.
On-line portfolio selection has attracted increasing interests in machine learning and AI communities recently. Empirical evidences show that stock's high and low prices are temporary and stock price relatives are likely to follow the mean reversion phenomenon. While the existing mean reversion strategies are shown to …
Paper describes an on-line PCA algorithm for real-time data analysis.
Tensor analysis improves structural health monitoring of complex aerospace systems.
Approximate variational inference has shown to be a powerful tool for modeling unknown complex probability distributions. Recent advances in the field allow us to learn probabilistic models of sequences that actively exploit spatial and temporal structure. We apply a Stochastic Recurrent Network (STORN) to learn robot …
KQT-EWMA monitors multivariate data streams online with flexible and practical change detection.
The paper develops methods for monitoring TPL machine health.
This paper re-examines the problem of parameter estimation in Bayesian networks with missing values and hidden variables from the perspective of recent work in on-line learning [Kivinen & Warmuth, 1994]. We provide a unified framework for parameter estimation that encompasses both on-line learning, where the model is c…
For large-scale industrial processes under closed-loop control, process dynamics directly resulting from control action are typical characteristics and may show different behaviors between real faults and normal changes of operating conditions. However, conventional distributed monitoring approaches do not consider the…
The paper describes an application of Aggregating Algorithm to the problem of regression. It generalizes earlier results concerned with plain linear regression to kernel techniques and presents an on-line algorithm which performs nearly as well as any oblivious kernel predictor. The paper contains the derivation of an …
A new method monitors unstructured 3D shapes without registration.
Study improves data quality assessment for structural monitoring data.
IDS optimizes regret in stochastic partial monitoring with linear rewards.
Deep learning improves cECG denoising for better cardiac health monitoring.
RAD detects anomalies in unreliable data streams with up to 98% accuracy.
In approachability with full monitoring there are two types of conditions that are known to be equivalent for convex sets: a primal and a dual condition. The primal one is of the form: a set C is approachable if and only all containing half-spaces are approachable in the one-shot game; while the dual one is of the form…
The method and characteristics of several approaches to the pricing of discretely monitored arithmetic Asian options on stocks with discrete, absolute dividends are described. The contrast between method behaviors for options with an Asian tail and those with monitoring throughout their lifespan is emphasized. Rates of…
Paper analyzes gradient descent with noisy data copies for linear regression, showing regularization and acceleration effects.
Study compares semi-supervised learning methods for anomaly detection in hydraulic systems.
Statistical arbitrage strategies, such as pairs trading and its generalizations, rely on the construction of mean-reverting spreads enjoying a certain degree of predictability. Gaussian linear state-space processes have recently been proposed as a model for such spreads under the assumption that the observed process is…
DCASE 2022 Task 2 tackles domain shifts in ASD for machine condition monitoring.
The problem of on-line off-policy evaluation (OPE) has been actively studied in the last decade due to its importance both as a stand-alone problem and as a module in a policy improvement scheme. However, most Temporal Difference (TD) based solutions ignore the discrepancy between the stationary distribution of the beh…
A principle for specialized decision-making divides complex problems into manageable parts.
Enhances monitoring of industrial systems with limited data.
Invariant Kähler metrics on line bundles are derived from the Calabi ansatz.
Deep learning reconstructs pressure fields and classifies leakage rates in CCS storage sites.
Novel method detects faults in helicopter transmissions using healthy data only.
Generalized cross validation (GCV) is one of the most important approaches used to estimate parameters in the context of inverse problems and regularization techniques. A notable example is the determination of the smoothness parameter in splines. When the data are generated by a state space model, like in the spline c…
We consider an on-line system identification setting, in which new data become available at given time steps. In order to meet real-time estimation requirements, we propose a tailored Bayesian system identification procedure, in which the hyper-parameters are still updated through Marginal Likelihood maximization, but …
Adaptive monitoring for AI systems detects and diagnoses shifts in data distribution.