Paper analyzes CoCos with short-term uncertainty and noisy firm reports.
problem Modeling conversion and default in CoCos under short-term uncertainty.
method Combines Duffie and Lando (2001) and Jeanblanc and Valchev (2005) models.
result Describes CoCo bond conversion and default using a short-term uncertainty model.
Framework improves PV forecasting by accounting for missing data uncertainty.
problem Uncertainty from missing data in PV power data.
method Combines stochastic multiple imputation with Rubin's rule.
result Improves prediction interval calibration without sacrificing point prediction accuracy.
Paper proposes a method for predicting any quantile of short-term electricity demand.
problem Uncertainty in power systems due to multiple factors.
method Proposes a novel general approach for distributional forecasting of short-term electricity demand.
result Demonstrates state-of-the-art distributional forecasting results for short-term electricity demand.
New method predicts soil moisture with uncertainty estimates.
problem Accurately predict soil moisture using deep learning models.
method Used Monte Carlo dropout with long short-term memory models.
result Successfully captures predictive error and detects dissimilarity.
A new model predicts wind speed using multiple meteorological variables.
problem Precise wind speed forecasting for wind power producers and grid operators.
method Multi-variable Stacked Long Short-Term Memory (LSTM) network.
result The proposed MSLSTM model outperforms traditional methods in wind speed prediction.
Paper predicts high-frequency futures return directions using mean-uncertainty methods.
problem Data imbalance in short-term price movements of futures markets.
method Employed mean-uncertainty logistic regression and support vector machines under sublinear expectation framework.
result Mean-uncertainty approaches outperform conventional methods in classification metrics and average returns.
New LSTM scheme incorporates prior knowledge and measurement uncertainties.
problem Overfitting and insufficient data for accurate time-dependent solutions.
method Sparse Bayesian training algorithm for automatic connection determination.
result Less prone to overfitting, smaller data set required for satisfying accuracy.
Bayesian LSTM for outlier detection reduces overfitting.
problem Overfitting and lack of uncertainty in LSTM networks.
method Approximate Bayesian estimation with Ensemble Kalman Filter and maximum likelihood.
result The method reduces overfitting and provides uncertainty estimates.
Proposes a deep learning approach for RUL estimation with uncertainty quantification.
problem Uncertainty in RUL estimation due to rarity of failures, unobserved future conditions, and sensor noise.
method Formulates RUL estimation as an Ordinal Regression problem and uses LSTM-OR networks. Quantifies uncertainty through an ensemble of models.
result LSTM-OR models yield more robust RUL estimates and high-quality uncertainty estimates.
Cryptocurrency prices predicted using LSTM, SVM, and polynomial regression.
problem Uncertainty in crypto coin values.
method Long Short Term Memory, Support Vector Machine, Polynomial Regression models.
result Support Vector Machine with linear kernel had the smallest mean square error.
HGP models traffic speed uncertainty better than current methods.
problem Highly variable measurement noise in crowdsourced traffic data.
method Heteroscedastic Gaussian processes (HGP) conditioned on sample size and traffic regime (SRC-HGP).
result SRC-HGP produces significantly better predictive distributions.
This paper improves radar performance against jammers using RL.
problem Improving radar performance against jammers.
method Reinforcement Learning (RL) with Deep Q-Network (DQN) and Long Short Term Memory (LSTM) networks.
result Softmax operator improves RL algorithm performance.
New model predicts financial market abnormalities using stock index uncertainties.
problem Forecasting abnormal financial fluctuations in the market.
method Quantitative analysis of mean and volatility uncertainties, constructing early warning indicators.
result Established a new abnormal fluctuations warning model.
Deep learning predicts traffic actors' future movements with uncertainty.
problem Predicting future states of traffic actors for autonomous vehicles.
method Deep learning models using raster images of actors' surroundings.
result Effective prediction of traffic actors' movements with uncertainty.
A method for multidimensional probabilistic electricity market forecasting is proposed.
problem Uncertainty in simultaneous multivariate predictions of electricity markets.
method Repeated resampling to estimate uncertainty of simultaneous multivariate predictions.
result The method provides highly accurate predictions and gains are largest when considering functions of variables.
Paper proposes a new method for hourly load forecasting using smart meter data.
problem Challenges in short-term load forecasting at fine granularity.
method Forecasting using Matrix Factorization (fmf) for hourly load forecasting.
result Significantly outperforms state-of-the-art methods in load forecasting.
Uber improves trip prediction with deep learning and uncertainty estimation.
problem Reliable uncertainty estimation for time series prediction at Uber.
method Proposes a novel end-to-end Bayesian deep model combining LSTM networks with probabilistic formulation.
result Successfully applied to large-scale time series anomaly detection at Uber.
Paper develops adaptive models for robust energy forecasting with missing data.
problem Operational models assume complete data; missing data can degrade forecast accuracy.
method Adaptive robust optimization and adversarial machine learning for missing data.
result Proposed models perform well even with short-term missing data and significantly outperform imputation with longer-term missing data.
BSG learns dynamic network spillovers and uncertainty quantification.
problem Identifying indirect spillovers and systemic risk in dynamic networks.
method Bayesian Spillover Graphs using FEVD and Bayesian time series models.
result Significant performance gains over baselines in identifying source and sink nodes.
New framework calibrates computer models using deep learning and quantile regression.
problem Uncertainty in computer model input parameters due to high-dimensional time series data.
method Deep neural network with long-short term memory layers for inverse modeling, quantile regression for interval predictions.
result Accurate point and interval estimates for input parameters in WRF-hydro model.
Study examines time-varying betas and their volatility in bank interest income and expense margins.
problem Understanding the variability of bank betas and their impact on net interest margins.
method Used state-space methods to estimate time-varying betas and conditional volatility.
result Substantial variation in interest income and expense betas, leading to varying net interest margin coefficients.
TSCoNet forecasts correlated geophysical fields with uncertainty estimates.
problem Accurate and reliable forecasts of correlated geophysical fields across many locations.
method Two-stage CNN-LSTM coupled with Gaussian copula.
result Calibrated prediction intervals without sacrificing point accuracy.
This study shows how trade policy uncertainty affects stock-T bill correlations.
problem The impact of trade policy uncertainty on stock-T bill relationships.
method Extended Dynamic Conditional Correlation (DCC) framework incorporating exogenous variables.
result Trade policy uncertainty significantly alters stock-T bill correlations, especially under specific political conditions.
The paper proposes a new method for probabilistic load forecasting using Bernstein-Polynomial Normalizing Flows.
problem High variability in short-term load forecasting at the low-voltage level due to fluctuating demand and increasing electrification.
method Flexible conditional density forecasting based on Bernstein polynomial normalizing flows with neural network control.
result Density predictions outperform traditional methods for 24h-ahead load forecasting.
An indicator detects short-term asset price bubbles using option quotes.
problem Detecting short-term asset price bubbles in financial markets.
method Martingale theory, SABR model, Bayesian statistical estimation.
result A closed-form martingale defect indicator for detecting asset price bubbles.
A novel transformer model improves classification of partially ordered sequences.
problem Classification of partially ordered sequences with uncertainty in timestamps.
method Developed a transformer-based model for partially ordered sequences, benchmarked against set models.
result Transformer-based model outperforms set models on three datasets.
Two novel models predict bus travel times with uncertainty, improving connection assurance.
problem Improving bus connection assurance by handling travel time uncertainty.
method Two novel approaches: Deep Quantile Regression (DQR) and Bayesian Recurrent Neural Networks (BRNN).
result DQR model performs best for 80%, 90%, and 95% prediction intervals, with small underestimation.
Develops a method for probabilistic simulation of renewable energy production at grid scale.
problem Uncertainty in short-term electricity generation from renewable assets.
method Probabilistic framework with asset calibration, hierarchical clustering, and Gaussianization.
result Full uncertainty quantification at asset and collection levels.
WindDragon forecasts wind power with deep learning.
problem Accurate short-term wind power forecasting is crucial for grid operation.
method Automated Deep Learning combined with Numerical Weather Predictions.
result Automated Deep Learning improves wind power forecasting accuracy.
Study improves stock return uncertainty prediction using Gaussian mixture distributions.
problem Improving prediction of stock market return uncertainty.
method Gaussian mixture distribution-based deep learning model.
result Superior performance in volatility estimation, especially during market volatility.
A Gaussian Process Ordinary Differential Equation framework for large continuous dynamical systems
problem Forecasting complex dynamical systems
method Kernel autonomous ODE approach based on Gaussian Processes and Quadratic Order Model Reduction
result Full model outperforms ROM methods in terms of accuracy or computational costs
Model combines long-term and short-term memory using conceptors.
problem Transfer between long-term and short-term memory.
method Recurrent neural network with gated reservoir for short-term memory and conceptors for long-term memory.
result Standard operations on conceptors allow combining long-term memories and describing their effect on short-term memory.
Improved GP model forecasts wireless demand extremes with better uncertainty quantification.
problem Forecasting extreme wireless demand spikes and troughs for network optimization.
method Designed a feature embedding kernel for Gaussian Process models.
result 32% reduction in short-term extreme value prediction error vs. S-ARIMA.
GP-LSTM models sequential data with LSTM inductive biases and scalable training.
problem Capturing recurrent structures in sequential data with standard kernel functions.
method Expressive closed-form kernel functions for Gaussian processes, optimized with semi-stochastic gradient procedure.
result State-of-the-art performance on benchmarks and autonomous driving application.
CERN improves activity recognition in videos with novel energy layer and p-values.
problem Recognizing group activities in videos at semantic levels.
method Two-level LSTM network with Confidence-Energy Recurrent Network (CERN).
result Superior performance compared to state-of-the-art approaches.
This research proposes a method to hedge freight rate risk in shipping markets under model uncertainty.
problem Managing freight risk in shipping markets under model uncertainty.
method The approach uses Wasserstein barycenter for modeling freight rates dynamics and optimal hedging strategy selection.
result The proposed method provides robust hedging strategies even in high noise cases.
Develops a two-stage conformal prediction method for Parkinson's disease medication needs.
problem Heterogeneous disease progression and treatment response in Parkinson's Disease.
method Two-stage conformal prediction framework with statistical guarantees.
result Quantifies uncertainty in medication needs predictions, improving clinical trust and quality of life.
Short-term probabilistic forecasting of German electricity imbalance prices.
problem Uncertainty in renewable energy capacity and electricity prices.
method Combining lasso with bootstrap, gamlss, and probabilistic neural networks for forecasting imbalance prices.
result Sophisticated methods improve empirical coverage of imbalance prices but do not substantially outperform the intraday continuous price index.
A new method forecasts hourly electricity prices considering product dynamics and limit order book signals.
problem High volatility and imbalance in power systems due to renewable energy and flexible demand.
method Incorporates short-term features from hourly and quarter-hourly products, including limit order book and neighboring product signals.
result Features from the limit order book are most influential, and neighboring product signals improve forecast accuracy.
The paper identifies short-term and long-term time scales in stock markets with and without structural breaks.
problem Understanding the nature of stock markets at short-term and long-term time scales.
method Applied Zivot and Andrews structural trend break model to identify structural breaks. Used empirical mode decomposition and Hurst exponent to analyze time scales.
result Identified short-term and long-term time scales in stock markets, with short-term scales within few days to 3 months and long-term scales greater than 5 months.
The paper introduces new portfolio rules beyond mean-variance, addressing asymmetry and uncertainty.
problem Optimizing portfolios with asymmetric returns and uncertainty in expected returns.
method Derives allocation rules for asymmetric Laplace distributed returns and random normal expected returns. Addresses singular covariance matrices and uncertainty in returns.
result Optimal worst-case scenario solution provides a convex alternative to risk parity, improving portfolio stability.
QLSTM outperforms LSTM in predicting KSE 100 index movements.
problem Predicting stock market movement in uncertain economic conditions.
method Used LSTM and QLSTM models on monthly data of economic indicators.
result QLSTM provided more accurate predictions of KSE 100 index values.
The study identifies a criterion for when stocks are not good investments, explaining it with a binomial tree model.
problem Determining when stocks are not good investments despite positive expected returns.
method A simple binomial tree model to explain the phenomenon of long-run asymmetry caused by skewed price distributions and volatility.
result The study finds that a certain ratio is a lower bound for when a stock is not a good investment, and empirical properties of this ratio are discussed.
Kernel method estimates long-term effects from short-term data.
problem Estimating long-term effects from short-term data in continuous actions.
method Kernel ridge regression to embed and extrapolate long-term effects.
result Uniform consistency and nonasymptotic error bounds for the estimator.
Study shows risk-averse investors have consistent ranking of risky assets.
problem Ranking of risky assets in short-term investments.
method Analyzes various decision problems regarding risky assets with continuous returns.
result Risk-averse decision makers have the same ranking over risky assets.
This paper proposes a framework to predict long-term trends and short-term fluctuations in multivariate time series.
problem Existing prediction methods often ignore the distinction between long-term trends and short-term fluctuations.
method The paper introduces a MTS forecasting framework that uses both original time series and its first difference to capture long-term trends and short-term fluctuations.
result The proposed method improves forecasting performance by using more supervision information.
Predicts short-term futures contract direction using neural networks and order flow data.
problem Challenges in predicting short-term directional movement of futures contracts.
method Engineering features from technical analysis, order flow, and order-book data; training a Tabnet neural network.
result Achieved an accuracy of 0.601 in predicting directional change on the Silver Futures Contract.
A new model for pricing ultra-short-term options with complex volatility patterns.
problem Complex pricing of ultra-short-term options due to oscillations in implied volatility.
method Edgeworth++ model with nonparametric stochastic volatility and deterministic shift extension.
result Fast and accurate closed-form option pricing for ultra-short-term options.