Papers Probabilistic Time Series Forecasting
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DecoVAE: a Lightweight Interpretable Trend-Seasonal VAE Framework for Efficient Probabilistic Time Series Forecasting
Probabilistic time series forecasting remains challenging, largely because modeling distinct trend and seasonal dynamics requires specialized approaches. Existing methods often fail to capture the unique inner properties…
Probabilistic Time Series ForecastingCLaST: Context-aware Contrastive VAE for Probabilistic Time Series Forecasting
Probabilistic forecasting models are widely used for time series forecasting in domains such as energy systems, finance, medicine, and transportation. In recent years, deep generative models have shown strong results on …
Probabilistic Time Series ForecastingMultivariate Time Series ForecastingProbRes: Volatility Learning for Probabilistic Time-Series Forecasting
Probabilistic time series forecasting has attracted increasing attention in financial applications due to the need to quantify risk and uncertainty in future observations. We propose ProbRes, a post-hoc probabilistic cal…
Probabilistic Time Series ForecastingParametric Prior Mapping Framework for Non-stationary Probabilistic Time Series Forecasting
Effectively modeling non-stationary dynamics in probabilistic multivariate time series(MTS) forecasting requires balancing expressiveness with robustness. Existing parametric approaches benefit from strong inductive bias…
Probabilistic Time Series ForecastingComputational EfficiencyBeyond Static Uncertainty: Modeling Temporal Uncertainty Dynamics for Probabilistic Time Series Forecasting
Real-world time series exhibit temporally structured uncertainty: volatility clusters in turbulent regimes, dissipates in stable periods, and shifts abruptly around structural breaks. Yet many probabilistic forecasting m…
Probabilistic Time Series ForecastingNoise Titration: Exact Distributional Benchmarking for Probabilistic Time Series Forecasting
Modern time series forecasting is evaluated almost entirely through passive observation of single historical trajectories, rendering claims about a model's robustness to non-stationarity fundamentally unfalsifiable. We p…
Probabilistic Time Series ForecastingStaTS: Spectral Trajectory Schedule Learning for Adaptive Time Series Forecasting with Frequency Guided Denoiser
Diffusion models have been used for probabilistic time series forecasting and show strong potential. However, fixed noise schedules often produce intermediate states that are hard to invert and a terminal state that devi…
Probabilistic Time Series ForecastingLet Experts Feel Uncertainty: A Multi-Expert Label Distribution Approach to Probabilistic Time Series Forecasting
Time series forecasting in real-world applications requires both high predictive accuracy and interpretable uncertainty quantification. Traditional point prediction methods often fail to capture the inherent uncertainty …
Probabilistic Time Series ForecastingComputational EfficiencyTimeGMM: Single-Pass Probabilistic Forecasting via Adaptive Gaussian Mixture Models with Reversible Normalization
Probabilistic time series forecasting is crucial for quantifying future uncertainty, with significant applications in fields such as energy and finance. However, existing methods often rely on computationally expensive s…
Probabilistic Time Series ForecastingBeyond MSE: Ordinal Cross-Entropy for Probabilistic Time Series Forecasting
Time series forecasting is an important task that involves analyzing temporal dependencies and underlying patterns (such as trends, cyclicality, and seasonality) in historical data to predict future values or trends. Cur…
Probabilistic Time Series ForecastingOrdinal ClassificationValue predictionBenchmarking Probabilistic Time Series Forecasting Models on Neural Activity
Neural activity forecasting is central to understanding neural systems and enabling closed-loop control. While deep learning has recently advanced the state-of-the-art in the time series forecasting literature, its appli…
Probabilistic Time Series ForecastingA Primer on Kolmogorov-Arnold Networks (KANs) for Probabilistic Time Series Forecasting
This work introduces Probabilistic Kolmogorov-Arnold Network (P-KAN), a novel probabilistic extension of Kolmogorov-Arnold Networks (KANs) for time series forecasting. By replacing scalar weights with spline-based functi…
Probabilistic Time Series ForecastingConditionally Whitened Generative Models for Probabilistic Time Series Forecasting
Probabilistic forecasting of multivariate time series is challenging due to non-stationarity, inter-variable dependencies, and distribution shifts. While recent diffusion and flow matching models have shown promise, they…
Probabilistic Time Series ForecastingFrom Samples to Scenarios: A New Paradigm for Probabilistic Forecasting
Most state-of-the-art probabilistic time series forecasting models rely on sampling to represent future uncertainty. However, this paradigm suffers from inherent limitations, such as lacking explicit probabilities, inade…
Probabilistic Time Series ForecastingRDIT: Residual-based Diffusion Implicit Models for Probabilistic Time Series Forecasting
Probabilistic Time Series Forecasting (PTSF) plays a critical role in domains requiring accurate and uncertainty-aware predictions for decision-making. However, existing methods offer suboptimal distribution modeling and…
Probabilistic Time Series ForecastingEnd-to-End Probabilistic Framework for Learning with Hard Constraints
We present a general purpose probabilistic forecasting framework, ProbHardE2E, to learn systems that can incorporate operational/physical constraints as hard requirements. ProbHardE2E enforces hard constraints by exploit…
Probabilistic Time Series Forecastingscoring ruleTime Series ForecastingUncertainty Quantification$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting
Probabilistic Time Series Forecasting (PTSF) plays a crucial role in decision-making across various fields, including economics, energy, and transportation. Most existing methods excell at short-term forecasting, while o…
Decision MakingProbabilistic Time Series ForecastingTime SeriesTime Series ForecastingEffective Probabilistic Time Series Forecasting with Fourier Adaptive Noise-Separated Diffusion
We propose the Fourier Adaptive Lite Diffusion Architecture (FALDA), a novel probabilistic framework for time series forecasting. First, we introduce the Diffusion Model for Residual Regression (DMRR) framework, which un…
Computational EfficiencyDenoisingProbabilistic Time Series Forecastingregression+2Non-stationary Diffusion For Probabilistic Time Series Forecasting
Due to the dynamics of underlying physics and external influences, the uncertainty of time series often varies over time. However, existing Denoising Diffusion Probabilistic Models (DDPMs) often fail to capture this non-…
DenoisingProbabilistic Time Series ForecastingTime SeriesTime Series ForecastingProbabilistic Time Series Forecasting of Residential Loads -- A Copula Approach
Predicting the time series of future evolutions of renewable injections and demands is of utmost importance for the operation of power systems. However, the current state of the art is mostly focused on mean-value time s…
Density EstimationProbabilistic Time Series ForecastingTime SeriesTime Series Forecasting