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.
Two ML approaches compare in recognizing tables from historical records.
Proposes a multi-modal attention network for better stock price prediction.
Genealogy research is the study of family history using available resources such as historical records. Ancestry provides its customers with one of the world's largest online genealogical index with billions of records from a wide range of sources, including vital records such as birth and death certificates, census re…
New method uses surrogate outcomes and single-record data to improve suicide risk modeling.
The link between different psychophysiological measures during emotion episodes is not well understood. To analyse the functional relationship between electroencephalography (EEG) and facial electromyography (EMG), we apply historical function-on-function regression models to EEG and EMG data that were simultaneously r…
Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network t…
Accurate real-time monitoring systems of influenza outbreaks help public health officials make informed decisions that may help save lives. We show that information extracted from cloud-based electronic health records databases, in combination with machine learning techniques and historical epidemiological information,…
This paper addresses the problem of predicting duration of unplanned power outages, using historical outage records to train a series of neural network predictors. The initial duration prediction is made based on environmental factors, and it is updated based on incoming field reports using natural language processing …
Databases of electronic health records (EHRs) are increasingly used to inform clinical decisions. Machine learning methods can find patterns in EHRs that are predictive of future adverse outcomes. However, statistical models may be built upon patterns of health-seeking behavior that vary across patient subpopulations, …
Paper identifies bias and strategic behavior in crowdsourced performance assessments.
We discuss several uses of blockchain (and, more generally, distributed ledger) technologies outside of cryptocurrencies with a pragmatic view. We mostly focus on three areas: the role of coin economies for what we refer to as data malls (specialized data marketplaces); data provenance (a historical record of data and …
Deep learning models predict stock prices with high accuracy.
Clinical models trained on EHRs degrade in performance over time due to data drift.
Study models extreme skew surges along French Atlantic coast.
Improves trial efficiency by adjusting for historical prognostic scores.
Nostradamus links climate and stock market performance.
Empirical study on long-term discount rates using historical bond prices.
The process of liquidity provision in financial markets can result in prolonged exposure to illiquid instruments for market makers. In this case, where a proprietary position is not desired, pro-actively targeting the right client who is likely to be interested can be an effective means to offset this position, rather …
Deep learning predicts readmissions from less structured data.
This paper suggests claim history will be deprecated in future auto insurance rates.
Develops fair clinical risk prediction models using counterfactual reasoning.
MedGraph learns patient visit embeddings from EMRs, capturing both attributes and temporal sequences.
Paper improves volatility forecasting for new issues and spin-offs.
This paper focuses on the problem of estimating historical traffic volumes between sparsely-located traffic sensors, which transportation agencies need to accurately compute statewide performance measures. To this end, the paper examines applications of vehicle probe data, automatic traffic recorder counts, and neural …
Model predicts unseen climate extremes to inform risk planning.
Proposes a time-aware attention model for CTR prediction.
Proposes a method to learn from historical data for personalized decision-making.
This paper presents deep learning models for NIFTY 50 stock price prediction.
ConCare personalizes healthcare predictions by capturing EMR features.
TripDecoder recovers metro routes and travel times from smart card data.
A technique uncovers latent causal relationships in multiple time series data.
A neural collaborative filtering method predicts corn hybrid yield performance.
We present a new approach to the problems of evaluating and learning personalized decision policies from observational data of past contexts, decisions, and outcomes. Only the outcome of the enacted decision is available and the historical policy is unknown. These problems arise in personalized medicine using electroni…
A central problem of Quantitative Finance is that of formulating a probabilistic model of the time evolution of asset prices allowing reliable predictions on their future volatility. As in several natural phenomena, the predictions of such a model must be compared with the data of a single process realization in our re…
The Rectified Linear Unit (ReLU) is a foundational activation function in artficial neural networks. Recent literature frequently misattributes its origin to the 2018 (initial) version of this paper, which exclusively investigated ReLU at the classification layer. This paper formally corrects the citation record by tra…
Deep learning models predict stock prices with high accuracy.
Ride sharing has important implications in terms of environmental, social and individual goals by reducing carbon footprints, fostering social interactions and economizing commuter costs. The ride sharing systems that are commonly available lack adaptive and scalable techniques that can simultaneously learn from the la…
Deep neural network predicts event ticket prices considering spatial-temporal data sparsity.
High frequency data in finance have led to a deeper understanding on probability distributions of market prices. Several facts seem to be well stablished by empirical evidence. Specifically, probability distributions have the following properties: (i) They are not Gaussian and their center is well adjusted by Levy dist…
Predicts traffic flow using reinforcement learning and sensor data.
Study investor sentiment and disagreement on StockTwits during COVID-19.
New estimator improves ATT estimation efficiency with external controls.
We study the statistics of record-breaking events in daily stock prices of 366 stocks from the Standard and Poors 500 stock index. Both the record events in the daily stock prices themselves and the records in the daily returns are discussed. In both cases we try to describe the record statistics of the stock data with…
Unified HS and related methods with explicit modeling assumptions.
Study finds public procurement awards, especially NGEU-funded ones, boost new lending.
Deep learning models generate music with arbitrary control strategies.
Record linkage involves merging records in large, noisy databases to remove duplicate entities. It has become an important area because of its widespread occurrence in bibliometrics, public health, official statistics production, political science, and beyond. Traditional linkage methods directly linking records to one…
We review recent advances on the record statistics of strongly correlated time series, whose entries denote the positions of a random walk or a Lévy flight on a line. After a brief survey of the theory of records for independent and identically distributed random variables, we focus on random walks. During the last few…