Robust forecast framework reduces distribution error by 63%.
problem Accurate distribution forecast for planning decisions.
method Backtest-based bootstrap and adaptive residual selection.
result Reduces Absolute Coverage Error by more than 63%.
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48 results for “robust forecasting”
Rating Multi-Modal Time-Series Forecasting Models (MM-TSFM) for Robustness Through a Causal Lenscs.LG
This paper rates robustness of multi-modal time-series forecasting models.
problem Robustness of AI systems in time-series forecasting is crucial for stakeholders.
method Causal analysis to assess robustness of MM-TSFM models.
result Multi-modal forecasting models are more robust than numeric models.
A robust machine learning approach forecasts U.S. Treasury yields, reducing risk for investors.
problem Noisy and uncertain U.S. Treasury yields pose risk to forecast users.
method Formulates yield curve forecasting as a distributionally robust problem, combining factor models and machine learning.
result Robust forecast combinations improve out-of-sample performance across different maturity periods.
Robust Transformer-Based One-Step Stock Index Forecasting via Shifted Data Augmentation
problem Robust stock index forecasting
method Modified Transformer architecture with Shifted Data Augmentation
result Best performance on benchmark datasets
Proposes MVG-CRPS for robust multivariate forecasting.
problem Outliers in multivariate forecasting lead to significant errors.
method Integrates CRPS for MVG distributions, optimizing with MVG-CRPS.
result Improves robustness, accuracy, and uncertainty quantification.
New method reduces forecasting error by up to 67% in various data types.
problem Outliers and model misspecification in online infinite hidden Markov models.
method Batched Robust iHMM (BR-iHMM) with bounded posterior influence function.
result Reduces one-step-ahead forecasting error by up to 67% in various data types.
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.
Paper develops a robust hedging framework to reduce market risk and uncertainty.
problem Managing uncertainty and risk exposure in portfolio management.
method Combines high-frequency realized variance, covariance measures, and autoregressive models for multi-step volatility forecasting. Uses a box-uncertainty robust optimization scheme to derive a closed-form solution for the robust hedge ratio.
result Robust hedge ratios are more stable and entail lower turnover than standard dynamic hedges, improving downside protection and risk-adjusted performance.
SPADE-S improves time series forecasting accuracy for low-magnitude and sparse data.
problem Challenges in forecasting time series with strong heterogeneity in magnitude and sparsity.
method SPADE-S is a robust forecasting architecture that reduces biases and improves overall prediction accuracy.
result SPADE-S outperforms existing state-of-the-art approaches across diverse use cases, improving forecast accuracy by up to 15%.
Social Security and other public policies can be viewed as a series of cash in and outflows that depend on parameters such as the age distribution of the population and the retirement age. Given forecasts of these parameters, policies can be designed to be financially stable, i.e., to terminate with a zero balance. If …
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.
Improved sales forecasting at various levels using ensemble methods.
problem Enhancing sales forecasting accuracy at different levels of e-commerce data.
method Hierarchical robust aggregation of sales forecasts using exponential smoothing and Holt's linear trend method.
result Better forecasts at subsubfamily, subfamily, and family levels compared to individual techniques.
New metrics improve probabilistic forecasting, especially for rare events.
problem Current evaluation frameworks for probabilistic forecasting assume independence and lack sensitivity to tail events.
method Proposed signature kernel-based metrics: Sig-MMD and CSig-MMD.
result These metrics capture complex dependencies and prioritize tail event prediction.
Merlin improves robustness of MTSF models to missing data.
problem Suboptimal forecasting performance due to unfixed missing rates in MTSF models.
method Offline knowledge distillation and multi-view contrastive learning.
result Merlin enhances robustness of MTSF models while preserving accuracy.
CoIFNet unifies imputation and forecasting for robust multivariate time series prediction with missing values.
problem Pervasive missing values degrade multivariate time series forecasting accuracy.
method CoIFNet integrates imputation and forecasting through Cross-Timestep Fusion and Cross-Variate Fusion modules.
result CoIFNet achieves 24.40% improvement over state-of-the-art methods at 0.6 point (block) missing rate.
ElasTST improves time-series forecasting across varying horizons.
problem Robust forecasting across different time horizons in varied industrial sectors.
method Elastic Time-Series Transformer (ElasTST) with non-autoregressive design, rotary position embedding, and multi-scale patching.
result ElasTST provides robust forecasts across varying horizons without retraining.
GraphSVR forecasts urban air pollution robustly across stations and seasons.
problem Nonlinear, nonstationary, spatiotemporally dependent urban air pollution forecasting challenges.
method Combines graph convolutional learning and support vector regression.
result GraphSVR improves predictive accuracy and maintains stable performance across seasons and outlier-prone episodes.
Paper improves online time series forecasting by combining natural gradient and robust t-distribution.
problem Online time series forecasting challenges in rapidly adapting to evolving data.
method Reframed neural network optimization as a parameter filtering problem, using natural gradient and Student's t likelihood.
result Natural Score-driven Replay (NatSR) achieves stronger forecasting performance than state-of-the-art methods.
SOR-Mamba improves Mamba for robust time series forecasting by minimizing channel order bias.
problem Robust time series forecasting with Mamba's sequential order bias.
method SOR-Mamba incorporates regularization to minimize channel order discrepancy and introduces CCM for channel correlation preservation.
result SOR-Mamba enhances robustness to channel order and improves forecasting accuracy.
Proposes a deep generative model for robust forecasting on sparse multivariate time series.
problem Forecasting on sparse multivariate time series with suboptimal results when sparsity is high.
method Dynamic Gaussian Mixture distribution for modeling latent clusters, using neural networks and gating mechanism.
result Demonstrates robust modeling of sparse multivariate time series with improved accuracy.
Decision-Focused Fine-Tuning of Time Series Foundation Models for Dispatchable Feeder Optimizationcs.LG
Improved forecast accuracy for energy systems through decision-focused fine-tuning.
problem Challenges in integrating forecast values into time series models for diverse and specific instances.
method Decision-focused fine-tuning within time series foundation models for dispatchable feeder optimization.
result Improvement of 9.45% in average total daily costs.
New method forecasts time series with changing variances.
problem Real-world processes with changing variances cannot be captured by classical models.
method State-space model with Markov switching variances, using online learning and expert aggregation.
result Proposed method outperforms traditional expert aggregation and is robust to misspecification.
As an important part of the power system, power load forecasting directly affects the national economy. The data shows that improving the load forecasting accuracy by 0.01% can save millions of dollars for the power industry. Therefore, improving the accuracy of power load forecasting has always been the pursuing goals…
Study introduces TeMoP model for better stock market predictions.
problem Decreasing prediction errors and robustness across datasets in machine learning models.
method Probabilistic multiple lag order model based on trend encoding.
result TeMoP model outperforms machine learning models in accuracy and stability across different stock indexes.
Deep learning improves PV generation quantile forecasting.
problem Accurate probabilistic forecasting of PV generation.
method Developed an encoder-decoder deep learning model for multi-output quantile PV forecasting.
result The model improves forecast quality and computational efficiency.
Develops RES metrics for stable rare-event forecasting evaluation.
problem Challenges in evaluating forecasts of rare events.
method Rare-event-stable (RES) metrics designed to maintain stable thresholds under extreme rarity.
result RES metrics maintain stable thresholds, consistent model rankings, and near-complete prevalence invariance.
FinCast is a foundation model for financial time-series forecasting that outperforms existing methods.
problem Challenges in financial time-series forecasting due to temporal non-stationarity, multi-domain diversity, and varying temporal resolutions.
method FinCast is a foundation model specifically designed for financial time-series forecasting, trained on large-scale financial datasets.
result FinCast exhibits robust zero-shot performance, effectively capturing diverse patterns without domain-specific fine-tuning.
A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecastingstat.ML
AEnbMIMOCQR generates robust multi-step ahead prediction intervals for time series data.
problem Generating reliable multi-step ahead prediction intervals for time series data.
method Adaptive ensemble batch multi-input multi-output conformalized quantile regression (AEnbMIMOCQR) based on conformal prediction principles.
result AEnbMIMOCQR provides close to exact coverage and robustness to distribution shifts.
Production forecasting is a key step to design the future development of a reservoir. A classical way to generate such forecasts consists in simulating future production for numerical models representative of the reservoir. However, identifying such models can be very challenging as they need to be constrained to all a…
An image-driven approach for time series forecasting.
problem Time-series forecasting as a computer vision task.
method Capture input data as an image and train a model to produce the subsequent image.
result Our method outperforms various baselines, including ARIMA, using image-based evaluation metrics.
A federated graph learning approach improves EV charging demand forecasting while protecting against cyberattacks.
problem Cybersecurity risk and data heterogeneity in EV charging demand forecasting.
method Federated Graph Neural Network (GNN) model with global attention mechanism and credit-based function.
result Enhanced robustness and prediction accuracy in EV charging demand forecasting.
FredNormer improves time series forecasting by adapting to frequency domain patterns.
problem Current normalization methods struggle with non-stationary time series due to their time-domain approach.
method FredNormer analyzes frequency components, adapts weights, and improves robustness.
result FredNormer boosts forecasting accuracy by 33.3% on ETTm2 dataset.
Transfer learning aims at building robust prediction models by transferring knowledge gained from one problem to another. In the semantic Web, learning tasks are enhanced with semantic representations. We exploit their semantics to augment transfer learning by dealing with when to transfer with semantic measurements an…
Paper introduces a neural framework for accurate energy forecasting.
problem Challenges of forecasting energy demand and supply due to variability of renewable sources and dynamic consumption patterns.
method Integrates Neural ODEs, graph attention, multi-resolution wavelet transformations, and adaptive learning of frequencies.
result Consistently outperforms state-of-the-art baselines in various forecasting metrics across diverse datasets.
MetaPhysiCa tackles robust physics-informed machine learning for OOD tasks.
problem Designing robust PIML methods for OOD forecasting tasks in physics.
method Meta-learning procedure for causal structure discovery including invariant risk minimization.
result Significantly outperforms existing PIML and deep learning methods in OOD tasks.
Improved probabilistic forecasts using behavioral transformations.
problem Improving accuracy and consistency of probabilistic asset price forecasts.
method Behavioral transformation of fundamental expectations to disentangle sentiment-induced biases.
result Substantial forecast gains across various models and risk-preferences.
Robust spatio-temporal GP framework for outlier-resilient predictions.
problem Outliers in spatio-temporal data degrade GP performance.
method Adapted and specialised RCGP framework for spatio-temporal settings.
result RCGP provides reliable spatio-temporal predictions with outliers.
Unified deep sequential and state-space models for robust option pricing with uncertainty.
problem Combining robustness to noise and uncertainty measurement in option pricing models.
method Unscattered reservoir smoother (URS) integrating deep sequential and state-space models.
result URS achieves competitive forecasting accuracy and uncertainty measurement in noisy datasets.
Forecast stock return distributions using neural networks.
problem Accurately modeling non-Gaussian stock return features.
method Two-stage quantile neural network with spline interpolation.
result Improved mean and variance forecasts compared to standard models.
This study compares two neural models for financial forecasting, showing their superiority.
problem Improving financial market trend predictions using neural networks.
method Systematic comparison of N-HiTS and N-BEATS with conventional models.
result N-HiTS and N-BEATS enhance forecast accuracy and robustness in financial time series data.
The article presents a new non-parametric approach for forecasting mortality and fertility using Gaussian process regression.
problem Precise forecasting of demographic movements in developed countries.
method Gaussian process regression with natural cubic spline and spectral mixture covariance functions.
result The approach shows significant improvements in forecasting precision and robustness.
Linking SV and PDV models for better volatility forecasts.
problem Improving volatility forecasting models.
method Assumed density filtering to map SV models to PDV representations, introducing calibration procedure.
result Improves in-sample fit and robust out-of-sample forecasts.
The paper addresses decision making with partially calibrated forecasts, offering a robust approach.
problem Developing a decision-making strategy for forecasts that are only partially calibrated.
method A minimax approach to mapping predictions to actions, considering worst-case distributions.
result The minimax optimal decision rule is to trust predictions and act accordingly, even for partially calibrated forecasts.
An accurate load forecasting has always been one of the main indispensable parts in the operation and planning of power systems. Among different time horizons of forecasting, while short-term load forecasting (STLF) and long-term load forecasting (LTLF) have respectively got benefits of accurate predictors and probabil…
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.
AI agents improve forecast combination in empirical economics.
problem Hidden researcher degrees of freedom in AI-generated code.
method Adapted agent-loop architecture to empirical economics, added holdout evaluation.
result Independent agent searches find better forecast methods than benchmarks.
Paper introduces probabilistic forecasting methods for cryptocurrency volatility.
problem Inadequate point forecasting methods for capturing full spectrum of volatility outcomes.
method Combines multiple base models (statistical and machine learning) to estimate conditional quantiles of cryptocurrency realized variance.
result QRS method outperforms sophisticated alternatives for Bitcoin volatility forecasting.
Adaptive volatility method improves probabilistic financial forecasting.
problem Probabilistic forecasting in financial markets.
method Adapts classical time-varying volatility models with online stochastic optimization.
result Ranked 5th in M6 financial forecasting competition.