A novel continual prediction model outperforms traditional one-time models in predicting AKI.
problem Optimally predicting AKI before it develops during a hospital stay.
method A novel continual prediction model that predicts AKI every time a patient's AKI-relevant variable changes in the EHR.
result The continual prediction model outperformed traditional one-time models, achieving a higher AUC of 0.724 compared to 0.653.
This study predicts flight delays for American Airlines using data mining and machine learning.
problem Flight delays and cancellations by American Airlines.
method Data mining and machine learning approaches, specifically Gradient Boosting Classifier Model.
result Achieved a maximum accuracy of 85.73% in predicting flight delays.
New L2D framework allows deferring specific parts of a sequence prediction to experts.
problem Current L2D methods defer entire predictions, which is not ideal for long sequences.
method Proposes token-level and one-time rejectors to defer specific outputs of a model prediction to experts.
result Granular deferrals achieve better cost-accuracy tradeoffs than whole deferrals.
Study links Hopf differentials to curvature line flows on time-like CMC surfaces.
problem Understanding the relationship between Hopf differentials and curvature line flows on time-like CMC surfaces.
method Investigation of Hopf differentials and curvature line flows on time-like CMC surfaces in Lorentzian 3-space forms.
result The index of a curvature line flow at an umbilic point depends on the remainder of the Hopf differential's order modulo four.
We define and discuss the notion of pseudospherical surfaces in asymptotic coordinates on time scales. Two special cases, namely dicrete pseudospherical surfaces and smooth pseudosperical surfaces are consistent with this description. In particular, we define the Gaussian curvature in the discrete case.
A new LSTM architecture improves time series forecasting efficiency.
problem Efficiency and accuracy in time series forecasting using linear models.
method Attention-free LSTM architecture for time series prediction.
result Improved prediction capacity and efficiency compared to LSTM.
We consider the scenario where the parameters of a probabilistic model are expected to vary over time. We construct a novel prior distribution that promotes sparsity and adapts the strength of correlation between parameters at successive timesteps, based on the data. We derive approximate variational inference procedur…
Given key performance indicators collected with fine granularity as time series, our aim is to predict and explain failures in storage environments. Although explainable predictive modeling based on spiky telemetry data is key in many domains, current approaches cannot tackle this problem. Deep learning methods suitabl…
Enhances VC startup success predictions using graph augmented time series models.
problem Challenges in predicting startup success due to limited financial data and subjective forecasts.
method Integrates inter-company relationships into time series analysis using GraphRAG.
result Significantly outperforms previous models in startup success predictions.
Unified Bayesian framework for missing data imputation and prediction in clinical time series.
problem High prevalence of missing values in clinical time series data.
method Unified Bayesian recurrent framework for imputation and prediction.
result Strong performance gains over state-of-the-art methods on mortality prediction tasks.
This paper surveys NLP techniques for predicting stock market movements.
problem Predicting stock market movements with volatility, seasonality, and time dependence.
method Survey of recent literature in NLP and ML for stock market prediction.
result Illustrates recent trends in stock market prediction research.
Optimizes bus schedules to improve on-time performance.
problem Improving on-time performance of public transit systems.
method Formulated as a single-objective optimization task, solved using greedy algorithm, GA, and PSO.
result Enhanced bus timetables leading to better on-time performance.
This work improves explanation quality for time series predictions by learning perturbations.
problem Explaining predictions on multivariate time series data with time dependencies.
method Learning both masks and associated perturbations to explain predictions.
result Learning perturbations significantly improves explanation quality on time series data.
QSurv models survival data without discretization, achieving high accuracy.
problem Intractable likelihood estimation for continuous-time survival models.
method QSurv uses numerical quadrature for cumulative hazard approximation and time-conditioned low-rank adaptation.
result QSurv achieves competitive predictive performance and interpretable hazard patterns.
Despite its importance, choosing the structural form of the kernel in nonparametric regression remains a black art. We define a space of kernel structures which are built compositionally by adding and multiplying a small number of base kernels. We present a method for searching over this space of structures which mirro…
Article proposes a profitable intraday trading strategy for Chinese stocks.
problem Intraday trading opportunities in Chinese stock market.
method Markowitz optimization and Multilayer Perceptron (MLP) for stock price prediction.
result Validation of Markowitz portfolio optimization and MLP for intraday stock price prediction.
Enhanced Neural ODEs outperform traditional models in image classification and video prediction.
problem Efficiently modeling time-varying dynamics in neural networks.
method Proposed a novel family of non-autonomous Neural ODEs with time-varying weights.
result Outperformed previous Neural ODE variants in speed and representational capacity.
Study improves healthcare time series imputation by considering structured missingness.
problem Structured missingness in clinical data impacts time series imputation models.
method Analysis of different masking strategies on imputation methods using PhysioNet Challenge 2012 dataset.
result Masking choices significantly affect imputation accuracy and clinical prediction.
Traditional human activity recognition (HAR) based on time series adopts sliding window analysis method. This method faces the multi-class window problem which mistakenly labels different classes of sampling points within a window as a class. In this paper, a HAR algorithm based on U-Net is proposed to perform activity…
We derive a logarithmic Sobolev inequality along the Ricci flow without any restriction on time, which depends only on the initial metric via rudimentary geometric data, assuming only that a certain first eigenvalue is positive. As a consequence we obtain a uniform Sobolev inequality along the Ricci flow without any re…
A CNN-based model improves stock price prediction accuracy.
problem Overfitting in image-based stock prediction models.
method SMSFR-CNN combining CNN and image features.
result SMSFR-CNN achieves high predictive accuracy on A-share stocks.
Tick is a statistical learning library for Python~3, with a particular emphasis on time-dependent models, such as point processes, and tools for generalized linear models and survival analysis. The core of the library is an optimization module providing model computational classes, solvers and proximal operators for re…
Study heat flow on changing surfaces, proving existence and uniqueness.
problem Existence and uniqueness of heat flow on time-varying manifolds.
method Establishes estimates for heat flow under minimal assumptions, focusing on logarithmic derivative of volume measure.
result Proves estimates hold for Ricci flow with scalar curvature bounded below, dependent only on initial data.
Adaptive weighting schemes enhance time-series data augmentation for financial and UCR datasets.
problem Limited size of time-series datasets hinders model performance.
method Two adaptive weighting schemes for automatic data augmentation.
result Improves annualized returns by over 50% on financial dataset and outperforms state-of-the-art on half of UCR datasets.
We propose a machine learning approach to address a key challenge in materials science: predicting how fractures propagate in brittle materials under stress, and how these materials ultimately fail. Our methods use deep learning and train on simulation data from high-fidelity models, emulating the results of these mode…
Paper compares deep learning models for financial series prediction.
problem Predicting financial time series with deep learning methods.
method Compares MLP, 1D CNN, stacked LSTM, attention networks, and weighted attention networks.
result Attention LSTM model produces a 0.76 hit ratio, superior to other methods.
We define and discuss the notion of pseudospherical surfaces in asymptotic coordinates on time scales. Thus we extend well known notions of discrete pseudospherical surfaces and smooth pseudosperical surfaces on more exotic domains (e.g, the Cantor set). In particular, we present a new expression for the discrete Gauss…
We study the phase transition of dynamical herd behaviors for the yen-dollar exchange rate in the Japanese financial market. It is obtained that the probability distribution of returns satisfies the power-law behavior with three different values of the scaling exponent 3.11 (one time lag τ = 1 minute), 2.81 (30 minut…
This paper enhances LSTM neural networks for multi-variable time series data, providing interpretable insights.
problem Accurate prediction of multi-variable time series data with interpretable insights.
method Variable-wise hidden states and a mixture attention mechanism to model the generative process of the target variable.
result Enhanced prediction performance by capturing the dynamics of different variables.
Study shows priority access in ELA auctions is less valuable due to volatility risks.
problem Impact of volatility on time-based transaction ordering policies.
method Single-round model with risk-averse bidders, tested using ETH price data.
result Priority access is discounted due to volatility and risk aversion.
Thermalizer stabilizes autoregressive models for long-term predictions in chaotic systems.
problem Long-term predictions in chaotic spatiotemporal systems are unreliable due to trajectory divergence.
method Diffusion models are used to implicitly estimate the score of an invariant measure, which stabilizes autoregressive emulators by applying denoising during inference.
result Thermalization extends the time horizon of stable predictions by an order of magnitude in chaotic systems.
Study compares LSTM and Transformer models in financial time series prediction.
problem Comparing LSTM and Transformer models for financial time series prediction.
method Various LSTM-based and Transformer-based models compared on financial tasks; DLSTM and new Transformer architecture designed.
result Transformer-based models show limited advantage in absolute price sequence prediction, while LSTM-based models perform better on difference sequences.
MARS model outperforms others in stock price prediction across sectors.
problem Developing accurate models for stock price prediction.
method Used time series, econometric, machine learning, and deep learning models on stock data.
result MARS model is the best performing model across IT, Banking, and Health sectors.
MD-CGAN models forecast time series with probabilistic posterior distributions.
problem Limited applications of GANs in time series forecasting, especially with probabilistic predictions.
method Mixture Density Conditional Generative Adversarial Model (MD-CGAN) using Gaussian mixture output.
result MD-CGAN outperforms benchmarks, especially in noisy time series.
We examine a network of learners which address the same classification task but must learn from different data sets. The learners cannot share data but instead share their models. Models are shared only one time so as to preserve the network load. We introduce DELCO (standing for Decentralized Ensemble Learning with CO…
New neural networks with variable time constants for better time-series prediction.
problem Improving neural network performance in time-series prediction.
method Constructing networks of linear dynamical systems modulated by nonlinear gates, using numerical differential equation solvers.
result Liquid Time-Constant Networks (LTCs) yield superior performance on time-series prediction tasks.
Safe-House secures DeFi by limiting losses and enhancing security.
problem Ongoing hacks and security concerns in DeFi.
method Safe-House uses blockchain principles to secure asset movements.
result Safe-House limits maximum one-time loss to specified limits.
In our previous work we showed that for an ancient solution to the Ricci flow with nonnegative curvature operator, assuming bounded geometry on one time slice, bounded entropy implies noncollapsing on all scales. In this paper we prove the implication in the other direction, that for an ancient solution with bounded no…
We develop a method to estimate the time to unsafe responses in LLMs.
problem Estimating the time to unsafe responses in large language models is challenging due to the rarity of unsafe outputs.
method We frame the problem as survival analysis and propose a calibration technique for constructing a lower predictive bound (LPB).
result Our method provides rigorous coverage guarantees and improves sample efficiency.
The paper proposes a method for interpretable mixture density estimation using a tree structure.
problem Complex probability distributions in machine learning models.
method Interpretable tree structure for mixture density estimation with fast inference.
result The method achieves both high speed and interpretability for mixture density estimation.
Paper analyzes and predicts Covid19 in Romania using neural networks and regime switching.
problem Inaccurate reported numbers and multiple influencing factors in pandemic prediction.
method Three-stage analysis using SIR model refined with neural networks and regime switching.
result Daily estimation of parameters and identification of regime turning points for predictions.
Modified jackknife method improves predictive inference for time series data.
problem Lack of exchangeability and temporal dependence in time series data.
method Leave-a-window-out (LWO) method modification of the jackknife.
result LWO method achieves valid coverage in time series models with mild temporal dependence.
Multi-class classification with a very large number of classes, or extreme classification, is a challenging problem from both statistical and computational perspectives. Most of the classical approaches to multi-class classification, including one-vs-rest or multi-class support vector machines, require the exact estima…
New model predicts banana disease risk from climate data.
problem Managing Black Sigatoka disease under climate change.
method Latent Neural ODEs to model infection dynamics.
result Superior generalization performance up to one month ahead.
The relation between time series irreversibility and entropy production has been recently investigated in thermodynamic systems operating away from equilibrium. In this work we explore this concept in the context of financial time series. We make use of visibility algorithms to quantify in graph-theoretical terms time …
Study preferences over uncertain time payments, finds growth-optimality better than expected utility theory.
problem Understanding how people make decisions with uncertain timing of payments.
method Normative model of growth-optimality, revisiting experimental evidence on time lotteries.
result Growth-optimality better explains experimental data on time lotteries than expected discounted utility theory.
Model predicts volatility and dependencies in EUA and energy prices.
problem Analyzing uncertainty and dependencies in European carbon and energy prices.
method Probabilistic multivariate conditional time series model with VECM-Copula-GARCH structure.
result Forecasting performance evaluated in an extensive rolling-window study.
DoFlow models time series data for causal forecasting and anomaly detection.
problem Forecasting and causal reasoning in multivariate time series.
method Flow-based generative model over causal DAGs.
result Accurate interventional and counterfactual forecasting, anomaly detection.