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.
Framework improves ETF volatility forecasting by adapting to market conditions.
problem Challenges in volatility forecasting due to shifting market conditions and varying model performance.
method Risk-sensitive specialist routing using online risk-sensitive evaluation and state-dependent gating.
result Reduces forecast loss by 24% and underprediction loss by 22% compared to rolling-best baseline.
Paper justifies ideal point forecasts as measurable, clarifying conditions for their existence.
problem Justifying ideal point forecasts as measurable random variables.
method Clarifying and establishing measurability conditions for a wide class of functionals.
result Ideal point forecasts are shown to be measurable, providing theoretical justification.
The study improves VaR forecast accuracy by modeling conditional quantile dynamics.
problem Improving the accuracy of Value-at-Risk (VaR) forecasts for time-varying quantiles.
method Time-varying modeling of VaR, evaluation via simulation, asymmetric Mean Absolute Deviation loss function.
result Substantial improvements in forecasting conditional quantiles by maintaining predicted quantile unchanged.
QBVAR improves oil price forecasting across quantiles, especially for downside risk.
problem Forecasting oil prices across different quantiles for better risk assessment.
method Quantile Bayesian Vector Autoregression (QBVAR) model.
result QBVAR improves median forecasts by 2-5% and left-tail forecast improvements of 10-25% during crisis episodes.
We present a simple approach to forecasting conditional probability distributions of asset returns. We work with a parsimonious specification of ordered binary choice regression that imposes a connection on sign predictability across different quantiles. The model forecasts the future conditional probability distributi…
FCNv2 robustness tested under noise and random initial conditions.
problem Assessing AI weather forecasting model robustness to input noise.
method Two experiments with varying noise levels and random initial conditions.
result FCNv2 preserves hurricane features under low to moderate noise, but underestimates intensity and persistence.
CVAE improves stock volume forecasting with advanced input variables.
problem Improving accuracy of daily stock volume forecasts.
method Conditional Variational Auto-Encoder (CVAE) with advanced input variables.
result CVAE generates non-linear forecasts with better accuracy and correlation to actual data.
CDLF predicts product life-cycles in cold-start phases with high accuracy.
problem Forecasting new products in early phases when data is scarce.
method Conditional Diffusion Life-cycle Forecaster (CDLF) combining static descriptors, reference trajectories, and new observations.
result CDLF outperforms classical models in accuracy and probabilistic forecasting.
Self-guiding diffusion models improve time series forecasting, refinement, and generation.
problem Improving time series forecasting, refinement, and generation.
method Unconditionally-trained diffusion model with self-guidance mechanism.
result TSDiff outperforms task-specific conditional forecasting methods and maintains generative performance.
TSFlow uses Gaussian processes to match priors for better time series forecasting.
problem Difficulties in aligning generative models' priors with time series data.
method Conditional flow matching (CFM) with Gaussian processes, optimal transport, and data-dependent priors.
result TSFlow produces high-quality unconditional samples and competitive forecasting results.
PRECOG predicts future interactions between AVs and other drivers.
problem Autonomous vehicles need to predict human drivers' intentions for safe road behavior.
method Probabilistic forecasting model trained on real and simulated data.
result Our model predicts future interactions more accurately than existing methods.
Paper proposes a new method for selecting the best hierarchical forecasting approach.
problem Selecting the best method for reconciling base forecasts in hierarchical time series.
method Conditional hierarchical forecasting using machine learning and time series features.
result Conditional hierarchical forecasting leads to significantly more accurate forecasts, especially at lower levels.
CatBoostLSS predicts entire conditional distributions for probabilistic forecasting.
problem Limited to predicting only the conditional mean, traditional CatBoost is improved.
method Models all moments of a parametric distribution (mean, location, scale, shape).
result Enhanced flexibility in data analysis and probabilistic forecasting.
System predicts ice formation to improve road safety.
problem Improving road safety through accurate ice formation prediction.
method Combining physical models and machine learning for data correction.
result Machine learning effectively corrects incorrect data in forecasts.
PSTN improves traffic condition forecasting with deep neural networks.
problem Challenges in accurately forecasting traffic conditions due to complex spatiotemporal correlations.
method Proposes PSTN with three modules: graph convolutional network, temporal convolutional network, and gated recurrent unit framework.
result Significantly outperforms state-of-the-art benchmarks in short-term traffic conditions forecasting.
DA improves solar wind forecasts by updating model boundary conditions.
problem Improving solar wind forecasting accuracy.
method Variational Data Assimilation with solar wind model and in-situ observations.
result DA forecasts are more accurate than non-DA forecasts, especially when STEREO-B's latitude is offset from Earth.
Proposes a method to forecast non-stationary time series.
problem Challenges of non-stationary conditional distributions in deep learning.
method Bayesian dynamic model + deep conditional distribution model.
result Adapts to non-stationary time series better than state-of-the-art solutions.
Develops a method to continuously audit black-box conditional quantile forecasts.
problem Continuous monitoring of black-box forecasts under changing data streams and regimes.
method Distribution-free and game-theoretic testing framework for non-i.i.d. losses.
result Derives finite-time detection guarantees for miscalibrated forecasts based on features.
In this article we present an approach that enables joint wind speed and wind power forecasts for a wind park. We combine a multivariate seasonal time varying threshold autoregressive moving average (TVARMA) model with a power threshold generalized autoregressive conditional heteroscedastic (power-TGARCH) model. The mo…
Market forecasts converge to true values if some agents are correct.
problem Convergence of market forecasts in dynamic prediction markets.
method Dynamic model of prediction market with agents making forecasts.
result Aggregated market forecasts converge to conditional expectations.
ProFITi model forecasts irregular time series with missing values using conditional flows.
problem Probabilistic forecasting of irregularly sampled multivariate time series with missing values.
method ProFITi model uses conditional normalizing flows and invertible layers to learn joint distributions conditioned on past observations and queried channels and times.
result ProFITi model provides 4 times higher likelihood than the previous best model.
A new framework evaluates deep learning vs classical forecasting methods for time series predictions.
problem Current forecasting model evaluation metrics fail to capture model performance differences.
method Proposes a novel framework for evaluating univariate time series forecasting models from multiple perspectives.
result Deep learning models like NHITS outperform classical methods in multi-step ahead forecasting but not in anomaly handling.
Paper proposes a method for estimating tropical cyclone intensity distribution using deep learning.
problem Lack of full accounting of prediction variability in single-point forecasts.
method Smooth model over target and covariates, logistic transformation for conditional density, case-control sampling approximation.
result Method provides insights into predicted response behavior, improving decision-making and policy.
Develops a new framework for joint portfolio risk forecasting.
problem Joint portfolio risk forecasting, especially for Value-at-Risk and Expected Shortfall.
method Semi-parametric multivariate framework with dynamic conditional correlation modeling.
result The proposed model outperforms existing approaches in risk forecasting.
Given a nonlinear model, a probabilistic forecast may be obtained by Monte Carlo simulations. At a given forecast horizon, Monte Carlo simulations yield sets of discrete forecasts, which can be converted to density forecasts. The resulting density forecasts will inevitably be downgraded by model mis-specification. In o…
Study shows awareness of reflexivity improves LLMs' financial forecasting accuracy.
problem Improving LLMs' ability to forecast financial markets during boom-bust cycles.
method Evaluated three LLMs under four conditions of reflexivity awareness in two market episodes.
result Reflexivity awareness improves forecasting accuracy differently across models and contexts.
ModelRadar evaluates forecasting models across multiple aspects.
problem Evaluating forecasting models using single scores hides relevant performance variations.
method ModelRadar, a framework for aspect-based evaluation of univariate time series forecasting models.
result NHITS performs best overall but its superiority varies with forecasting conditions.
Machine learning improves beta forecasts, enhancing equity valuation and portfolio performance.
problem Improving beta forecasts for better equity valuation and portfolio performance.
method Using machine learning on a large cross-section of US stocks with various firm characteristics.
result Machine learning improves out-of-sample performance of asymmetric beta measures.
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.
ProbRes calibrates probabilistic forecasts by learning volatility dynamics.
problem Quantifying risk and uncertainty in time series forecasting.
method ProbRes learns conditional mean and volatility separately, generating well-calibrated prediction intervals.
result ProbRes accurately captures predictive distributions and produces well-calibrated prediction intervals.
Improved Hawkes model forecasts extreme financial returns more accurately.
problem Forecasting extreme tail events in financial log-returns.
method 2T-POT Hawkes model with multiple exceedance thresholds.
result 2T-POT Hawkes model outperforms GARCH-EVT model in risk forecasting.
Improved tail risk forecasting model for assets using CAViaR with spillover effects.
problem Improving tail risk forecasting across assets.
method Component-based CAViaR model with spillover effects, decomposing risk into proper and spillover components.
result Spillover effects significantly improve out-of-sample tail risk forecasts.
Unified probabilistic gradient boosting for entire conditional distribution modeling.
problem Creating accurate probabilistic forecasts from regression tasks.
method Unified probabilistic gradient boosting framework using XGBoost and LightGBM, modeling conditional moments or CDF via Normalizing Flows.
result Achieves state-of-the-art forecast accuracy.
The paper synthesizes the mathematics of modeling the future.
problem Modeling the future
method Unified mathematical synthesis
result Explicit connection of classical objects into a unified forecasting calculus
AutoPQ automates quantile forecasting for smart grids, reducing workload and environmental impact.
problem Accurate and unbiased uncertainty quantification in probabilistic forecasting for smart grid operations.
method AutoPQ uses a conditional Invertible Neural Network (cINN) to generate quantile forecasts from point forecasts, automating model selection and hyperparameter optimization.
result AutoPQ outperforms state-of-the-art methods while reducing computational effort and environmental impact.
A new model forecasts Value-at-Risk using NIG distribution and dynamic scores.
problem Forecasting Value-at-Risk (VaR) in financial markets.
method Proposes a parametric forecasting model based on the normal inverse Gaussian distribution (NIG) incorporating intraday information.
result The model outperforms traditional GARCH models, especially in high-risk scenarios.
New model forecasts power consumption with high accuracy over months to years.
problem Probabilistic forecasting of power consumption in a middle-term horizon.
method Combines traditional time-series analysis with weather conditions using Gaussian Process.
result Promising results in Out-of-Sample density forecasts up to one year.
The paper introduces a machine learning method to forecast market direction using efficient frontier coefficients.
problem Improving asset return estimation for portfolio optimization.
method Monthly directional market forecast using an online decision tree trained on efficient frontier coefficients.
result The method outperforms baseline portfolios and other feature sets.
Paper proposes MAST to identify stress conditions in forecasting models.
problem Improving reliability and transparency of univariate forecasting models under stress.
method Meta-learning and data augmentation approach to predict stress conditions.
result MAST identifies conditions leading to large errors in forecasting models.
HCNAF models complex conditional distributions for probabilistic occupancy forecasting.
problem Modeling complex conditional probability density functions for occupancy forecasting.
method Hyper-Conditioned Neural Autoregressive Flow (HCNAF) combining AF and hyper-network.
result HCNAF achieves state-of-the-art performance in self-driving datasets.
A new model forecasts optimal portfolio weights from high-frequency data.
problem Forecasting optimal portfolio weights from high-frequency data.
method Dynamic Conditional Weights (DCW) model for portfolio weights dynamics.
result DCW model outperforms other models in portfolio allocations and measures.
Transformer-based models overfit financial time series data, leading to increased prediction variance.
problem Forecast collapse of transformer-based models under squared loss in financial time series.
method Theoretical analysis and numerical experiments on high-frequency EUR/USD exchange rate data.
result Increased model expressivity in Transformer-based models leads to spurious fluctuations without reducing bias, resulting in higher prediction variance.
Paper uses econometrics time series model with T-student Distribution for short-term load forecasting.
problem Accurate short-term load forecasting for optimizing electrical sources and protecting energy.
method Uses SARIMA-GARCH model with T-student Distribution to forecast electric load.
result The proposed model outperforms the ARIMA model with Normal Distribution.
XGBoostLSS extends XGBoost to predict entire conditional distributions.
problem Predicting conditional mean only limits model flexibility.
method XGBoostLSS models all moments of a parametric distribution.
result Enhanced flexibility and additional insights from data.
This work combines recurrent models with diffusion for probabilistic time series forecasting.
problem Scalability and capturing high-dimensional distributions and cross-feature dependencies in time series forecasting.
method Combines recurrent neural networks' efficiency with diffusion models' probabilistic modeling, using stochastic interpolants and conditional generation.
result Offers scalable probabilistic time series forecasting methods.
Neural Lévy model improves risk and density forecasting for financial returns.
problem Financial returns exhibit heavy tails, volatility clustering, and jumps.
method Proposes a neural Lévy jump-diffusion framework that learns conditional drift, diffusion, jump intensity, and size distribution.
result Demonstrates improved calibration, sharper tail control, and risk reduction.
CauSTream forecasts streamflow by integrating causal graphs for better interpretability.
problem Streamflow forecasting lacks interpretability and generalization due to fixed causal models.
method CauSTream learns causal graphs for meteorological forcings and routing dependencies.
result CauSTream outperforms existing methods, especially at longer forecast windows.