A monitoring procedure improves machine learning forecasts for digital platforms.
problem Maintaining accurate and stable forecasts for data streams at digital platforms.
method Developed a monitoring procedure to determine when to retrain machine learning algorithms.
result Monitor-based retraining produces accurate forecasts compared to benchmarks.
The U.S. water distribution system contains thousands of miles of pipes constructed from different materials, and of various sizes, and age. These pipes suffer from physical, environmental, structural and operational stresses, causing deterioration which eventually leads to their failure. Pipe deterioration results in …
Neural network-based post-processing improves ensemble forecast sharpness
problem Reducing the width of central prediction intervals in ensemble forecasts
method Extending loss function with a penalty term
result 8.2%-12.5% reduction in width of central prediction interval
Improves model predictability by mixing forecasts and orthogonalizing models.
problem Redundant models contaminate model space and degrade predictive performance.
method Principal Component Analysis for model orthogonalization in Bayesian forecast mixing.
result Better prediction accuracy and excellent uncertainty quantification.
This paper improves QoS metric prediction in DTNs using diffusion models.
problem Improving QoS metric prediction in Delay-Tolerant Networks (DTNs) to enhance network performance.
method Formulates QoS metric prediction as a probabilistic forecasting problem on multivariate time series, incorporating latent temporal dynamics.
result The proposed approach outperforms traditional methods in QoS metric prediction for DTNs.
Random forest model predicts sewer pipe deterioration with high accuracy.
problem Challenges in predicting and scheduling sewer pipe inspections.
method Random forest classification model for sewer pipe condition prediction.
result Model achieved excellent AUC of 0.81 in a case study for City of LA.
Quantile gradient boosted trees outperform other models in predicting NO2 concentration distributions.
problem Forecasting high NO2 concentration episodes for effective air quality management.
method Compared 10 probabilistic forecasting models for NO2 concentration prediction.
result Quantile gradient boosted trees model outperformed others in predicting NO2 concentration distributions.
We focus on wind power modeling using machine learning techniques. We show on real data provided by the wind energy company Ma{ï}a Eolis, that parametric models, even following closely the physical equation relating wind production to wind speed are outperformed by intelligent learning algorithms. In particular, the CA…
Novel framework identifies pump-specific deterioration rates using Bayesian hierarchical hazard modeling and causal discovery.
problem Challenges in asset management due to heterogeneous deterioration rates in pump equipment.
method Bayesian hierarchical hazard modeling with causal discovery, GPU-accelerated No-U-Turn Sampling (NUTS), and DirectLiNGAM.
result Identified striking heterogeneity in deterioration rates, with negative effects 400 times larger than positive effects.
The paper monitors model deterioration using uncertainty estimation.
problem Challenges in monitoring deployed machine learning models.
method Non-parametric bootstrapped uncertainty estimates and SHAP values.
result The approach detects model deterioration and uncertainty effectively.
Predicting the remaining useful life of machinery, infrastructure, or other equipment can facilitate preemptive maintenance decisions, whereby a failure is prevented through timely repair or replacement. This allows for a better decision support by considering the anticipated time-to-failure and thus promises to reduce…
Proposes a framework to explain KS deterioration in credit risk models.
problem Inconsistent and ad hoc diagnosis of KS decline in credit risk models.
method Counterfactual diagnostic framework attributing KS decline to sampling variability, portfolio composition, covariate shift, and residual deterioration.
result The proposed approach provides more interpretable and governance-relevant explanations than threshold-based review alone.
DBNs improve ES and SES estimation for market risk, but tail behavior remains challenging.
problem Optimizing ES and SES estimation for market risk in banking.
method Extended DBNs for 10-day ES and SES estimation using S&P 500 index.
result DBNs perform comparably to historical simulation but struggle with tail behavior.
Proposes efficient algorithm for system-level I&M decisions under uncertainty.
problem Optimal management strategies for deteriorating civil engineering systems.
method Factored partially observable Markov decision process with Bayesian networks and DDMAC reinforcement learning.
result DDMAC policies offer substantial benefits over heuristic approaches in system-level cost optimization.
Accelerated gradient method's stability deteriorates exponentially with steps.
problem Algorithmic stability of Nesterov's accelerated gradient method.
method Analysis of two notions of algorithmic stability for Nesterov's accelerated gradient method.
result Stability of Nesterov's accelerated method deteriorates exponentially with the number of gradient steps.
Federated CTMC model estimates bridge deterioration hazards without sharing raw data.
problem Bridge inspection data privacy and cross-organizational data sharing constraints.
method Federated CTMC hazard model with local optimization and FedAvg aggregation.
result Federated model converges on global benchmark parameters without data transfer.
Study optimizes inspection and monitoring of deteriorating structures using POMDPs.
problem Optimizing decision-making for deteriorating engineering systems with uncertain observations.
method Developed theoretical and computational foundations for Value of Information (VoI) and Value of Structural Health Monitoring (VoSHM) in POMDPs.
result POMDP policies inherently leverage VoI to guide optimal observational actions, improving long-term cost.
Machine learning for healthcare often trains models on de-identified datasets with randomly-shifted calendar dates, ignoring the fact that data were generated under hospital operation practices that change over time. These changing practices induce definitive changes in observed data which confound evaluations which do…
Training on some out-of-distribution data improves generalization error before it deteriorates.
problem Generalization error improves with some out-of-distribution data but deteriorates with more.
method Synthetic datasets and deep networks on computer vision benchmarks.
result Non-monotonic trend in generalization error with OOD samples.
The Lasso is a computationally efficient regression regularization procedure that can produce sparse estimators when the number of predictors (p) is large. Oracle inequalities provide probability loss bounds for the Lasso estimator at a deterministic choice of the regularization parameter. These bounds tend to zero if …
Machine learning improves early detection of patient deterioration in Brazilian hospitals.
problem Challenges in recognizing clinical deterioration in hospital settings.
method Application of machine learning to analyze EHR data from multiple hospitals.
result Machine learning models outperformed traditional protocols by 25 percentage points in AUC.
The paper develops a method to predict the latent deterioration phase in limit order books before stress is observed.
problem Limit order books can transition rapidly from stable to stressed conditions, making it difficult to detect the latent deterioration phase.
method The paper formalizes a three-regime causal data-generating process and proposes a trigger-based detector combining MAX aggregation of complementary signal channels, a rising-edge condition, and adaptive thresholding.
result The proposed method achieves mean lead-time of +18.6 timesteps with perfect precision and moderate coverage, outperforming classical change-point and microstructure baselines.
Study quantifies firm risks from nature decline, showing significant equity losses.
problem Estimating the financial impact of nature deterioration on companies.
method Developed metrics (Country Degradation Index, Nature Risk Score) and assessed five environmental hazards.
result Global equities lose 26.8% in a nature decline scenario, with worst firms losing 75%.
Study on neural networks' performance in sequential task learning.
problem Understanding the performance of neural networks in sequential task learning.
method Theoretical analysis of generalization performance in continual learning using statistical mechanical analysis of kernel ridge-less regression.
result Characteristic transitions from positive to negative transfer observed in neural networks.
Monotonic neural additive models simplify machine learning for credit scoring.
problem Complex machine learning methods make models less transparent and fair.
method Introducing monotonic neural additive models that simplify neural networks while maintaining regulatory compliance.
result Monotonic neural additive models achieve similar accuracy to complex neural networks but are more transparent and fair.
Proposes LAE-EnKF for improved nonlinear data assimilation.
problem Performance of EnKF deteriorates for strongly nonlinear dynamics.
method Reformulates assimilation in a learned latent space with linear dynamics.
result LAE-EnKF yields more accurate and stable assimilation.
Theoretical and empirical taxonomy of imbalance in binary classification.
problem Class imbalance degrades binary classification performance.
method Proposed a principled framework based on three scales: imbalance coefficient, sample-dimension ratio, and intrinsic separability. Derived closed-form Bayes errors and analyzed degradation across models.
result The triplet (η, κ, Δ) provides a model-agnostic explanation of imbalance-induced deterioration.
Synthetic augmentation improves financial machine learning performance in variance-dominant regimes.
problem Data scarcity in financial machine learning.
method Formalized synthetic augmentation, introduced size-matched null augmentation, and developed a non-parametric block permutation test.
result Synthetic augmentation is beneficial only in variance-dominant regimes, such as persistent volatility forecasting.
Develops a minimax optimal estimator for system stability under distribution shift.
problem Ensuring system reliability under changes in the underlying environment.
method Minimax optimal estimation of stability defined in terms of acceptable performance degradation.
result Characterizes the minimax convergence rate and demonstrates practical utility.
A new neural network model identifies hysteresis universally.
problem Inability of existing models to simulate hysteresis universally.
method Inspired by the Preisach model, an Extended Preisach Neural Network (EPNN) is introduced with two hidden layers and a hybrid training algorithm.
result EPNN successfully identifies various hysteresis phenomena from different fields.
Study shows price impact increases with order-flow imbalance, using machine learning.
problem Understanding price impact in financial markets.
method Empirical investigation using Kyle's model and machine learning.
result Machine learning models can predict market impact more accurately than traditional methods.
Robust policies improve ICU transfer outcomes by predicting patient deterioration.
problem Higher mortality rates for unplanned ICU transfers.
method Markov Decision Process model to predict patient severity and optimize transfer policies.
result Robust policies are more aggressive in transferring patients than nominal policies, improving overall patient care.
Optimal reconciliation keeps some forecasts unchanged in hierarchical forecasting.
problem Keeping some forecasts unchanged in hierarchical forecasting.
method Formulates a method to keep some forecasts unchanged in a hierarchical forecasting system.
result Preserves unbiasedness and non-negativity of forecasts.
EEGNN improves graph neural networks by enhancing graph structure.
problem Mis-simplification of graphs by removing self-loops and unweighted edges reduces GNN performance.
method Proposes EEGNN framework using DMPGM for better graph structural information.
result EEGNN achieves significant performance improvement over baselines.
Short-term load forecasting is a critical element of power systems energy management systems. In recent years, probabilistic load forecasting (PLF) has gained increased attention for its ability to provide uncertainty information that helps to improve the reliability and economics of system operation performances. This…
Combining forecasts of 16 ED causes improves accuracy and stability.
problem Forecasting accuracy and stability for ED admissions is poor due to model uncertainty and limited data.
method High-dimensional forecast combinations of 16 cause-specific ED forecasts using extensive covariates.
result Forecast combinations yield forecast accuracies of 3.81%-23.54% across causes, outperforming individual models in 50% of scenarios.
New approach measures systemic risk by absorbing shocks before financial systems deteriorate.
problem Systemic risk evaluation without considering initial shocks.
method Linearized DebtRank and spectral graph theory for localized and uniform shocks; Monte Carlo simulations for heterogeneous shocks.
result Explicit computation and clear visualization of financial distress onset.
Conditional forecasts improve performative prediction accuracy.
problem Performative predictions undermine standard forecasting methods.
method Condition forecasts on covariates to make them forecast-invariant.
result Proper scoring rules fail under conditioning, but two solutions are identified.
Study improves seasonal forecasts using deep learning.
problem Challenges in generating large forecast ensembles and limited observations for verification.
method Developed a probabilistic deep neural network model.
result Demonstrated favorable skill compared to state-of-the-art dynamical forecast systems.
For2For combines forecasts to improve time series forecasting.
problem Improving time series forecasting accuracy.
method Combines standard forecasting methods and machine learning models using forecasts as features.
result Outperforms all submissions in the M4 competition for quarterly series and most monthly series.
Activity and motion analysis has the potential to be used as a diagnostic tool for mental disorders. However, to-date, little work has been performed in turning stratification measures of activity into useful symptom markers. The research presented in this thesis has focused on the identification of objective activity …
Two new methods improve forecasting of functional time series data.
problem Forecasting of functional time-dependent data.
method Functional Singular Spectrum Analysis (FSFA) based forecasting methods.
result Our methods outperform existing algorithms for periodic stochastic processes.
Deep learning improves time series forecasting, outperforming other methods.
problem Improving time series forecasting accuracy.
method Deep learning models for time series prediction.
result Deep learning models consistently outperform other methods in forecasting competitions.
Nowadays, with the unprecedented penetration of renewable distributed energy resources (DERs), the necessity of an efficient energy forecasting model is more demanding than before. Generally, forecasting models are trained using observed weather data while the trained models are applied for energy forecasting using for…
MPANF improves naive forecast by incorporating directional information.
problem Challenging to surpass naive forecast in financial time series.
method Combines naive forecast with movement prediction and accuracy.
result MPANF generally outperforms common benchmarks.
Simplifies forecast combination by using diversity of out-of-sample forecasts.
problem Estimating optimal weights for forecast combinations is challenging.
method Use out-of-sample forecasts to extract features and calculate weights for forecast combination.
result Achieves superior forecasting performance in point forecasts and prediction intervals.
Develops forecast hedging for improved calibration of forecasts.
problem Improving the accuracy of forecasted frequencies.
method Combines deterministic and stochastic approaches to forecast hedging.
result Ensures expected track record can only improve.
Microdata improves inflation forecasts after major shocks, study finds.
problem Forecasting inflation in a non-stationary environment with microeconomic data.
method Developed a scan test to detect periods of micro forecast outperformance, combined with adaptive machine learning.
result Micro forecasts improve inflation predictions after major shocks, especially after 2020.