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.
Firms delay write-downs for adverse macroeconomic and industry outcomes but not for firm-specific issues.
A very simple event frequency approximation algorithm that is sensitive to event timeliness is suggested. The algorithm iteratively updates categorical click-distribution, producing (path of) a random walk on a standard -dimensional simplex. Under certain conditions, this random walk is self-similar and corresponds …
Ever growing volume and velocity of data coupled with decreasing attention span of end users underscore the critical need for real-time analytics. In this regard, anomaly detection plays a key role as an application as well as a means to verify data fidelity. Although the subject of anomaly detection has been researche…
TIP-Search optimizes market prediction accuracy and timeliness under uncertain load.
The timeliness of detection of a sepsis event in progress is a crucial factor in the outcome for the patient. Machine learning models built from data in electronic health records can be used as an effective tool for improving this timeliness, but so far the potential for clinical implementations has been largely limite…
Bayesian approach models neurodegenerative diseases without clinical labels.
Generative AI boosts analyst reports but increases forecast errors.
Financial markets are extremely data-driven and regulated. Participants rely on notifications about significant events and background information that meet their requirements regarding timeliness, accuracy, and completeness. As one of Europe's leading providers of financial data and regulatory solutions vwd processes a…
Proposes a transformer-based approach for anomaly detection in time series data.
To be prepared against cyberattacks, most organizations resort to security information and event management systems to monitor their infrastructures. These systems depend on the timeliness and relevance of the latest updates, patches and threats provided by cyberthreat intelligence feeds. Open source intelligence platf…
A new method improves recommendation accuracy by learning from multiple networks and time-dependent user preferences.
This paper benchmarks econometric and machine learning methods in nowcasting GDP growth.
Indoor localization based on SIngle Of Fingerprint (SIOF) is rather susceptible to the changing environment, multipath, and non-line-of-sight (NLOS) propagation. Building SIOF is also a very time-consuming process. Recently, we first proposed a GrOup Of Fingerprints (GOOF) to improve the localization accuracy and reduc…
The study maps ML quality dimensions to fairness, enhancing the QF4SA framework.
Develops an LLM-based agent for superior cryptocurrency trading.
Explainable recommendation is far from being well solved partly due to three challenges. The first is the personalization of preference learning, which requires that different items/users have different contributions to the learning of user preference or item quality. The second one is dynamic explanation, which is cru…
This study proposes a new model for predicting financial distress in SMEs using machine learning.
Study categorizes time series anomaly detection metrics based on evaluation challenges.
The prevalence of online media has attracted researchers from various domains to explore human behavior and make interesting predictions. In this research, we leverage heterogeneous social media data collected from various online platforms to predict Taiwan's 2016 presidential election. In contrast to most existing res…
HOLMES improves real-time model serving for ICU patients, balancing accuracy and speed.
This paper proposes a communication-efficient deep anomaly detection framework for industrial IoT.
Polymarket users exploit mispriced assets for profit.