arXiv:2505.17847v3 Announce Type: replace-cross Abstract: Training time-series forecasting models poses unique challenges in loss function design. Most existing approaches adopt temporal mean squared...
The paper introduces Wasserstein Exponential Smoothing (WES), a single‑parameter recursive method for forecasting distributional time series on ℝ. WES updates forecast distributions along Wasserstein geodesics, allowing direct application to empirical distributions without parametric modeling. In high‑frequency equity‑index return and household electricity‑demand data, WES achieves the lowest one‑step‑ahead Wasserstein prediction error among existing benchmarks and is retained in the 90% model confidence set for all 20 series examined.
The paper investigates multi‑horizon forecasting of event sequences as a conditional generative modeling problem, evaluating forecasts for both prediction accuracy and distributional realism. It introduces a benchmark with metrics, baselines, and real‑world datasets, revealing a trade‑off between accuracy and realism. The authors propose Wasserstein BaryBooster, an algorithm that combines samples from a realism‑strong diffusion model with a state‑of‑the‑art predictor to convert distributional realism into improved prediction accuracy.
AdaCast introduces a conditional parameter generation framework that tailors time‑series forecasting models to each input by producing low‑rank updates for a frozen pretrained foundation model. Unlike static adaptation methods that apply a single set of updates to all inputs, AdaCast generates input‑specific updates during both training and inference. Experiments on six public benchmarks show that AdaCast consistently outperforms static baselines in in‑domain forecasting and improves zero‑shot generalization to unseen datasets across domains.
arXiv:2402.02399v3 Announce Type: replace-cross Abstract: Time series modeling presents unique challenges due to autocorrelation in both historical data and future sequences. While current research p...
arXiv:2603.19899v2 Announce Type: replace-cross Abstract: Autocorrelation is a common property of time-series, where each observation is dependent on its predecessors. In deep time-series forecasting...
arXiv:2609.09586v1 Announce Type: new Abstract: Time series foundation models (TSFMs) are increasingly pre-trained on synthetically generated time series trajectories, where the data generating proce...
The paper presents a deep learning framework, WGAN‑CLP, that forecasts voltage time‑series under anomalies by employing a Wasserstein GAN with a progressive Lipschitz penalty and an adaptive kernel critic. It processes a sliding window of data, predicts subsequent values, reconstructs the stream, and dynamically adjusts its kernel for each new window. Experiments on voltage and energy datasets show the model surpasses benchmark methods in accuracy and robustness, as measured by power ratios, correlation coefficient, and relative RMSE.
arXiv:2609.38058v1 Announce Type: cross Abstract: Time Series Foundation Models (TSFMs) currently provide state-of-the-art results in forecasting tasks. They are available out-of-the-box and rely on...
The paper introduces SOL, a new metric for measuring gaps between text distributions using double sliced Wasserstein distances. It represents each sequence as an empirical measure of hidden states from a fixed transformer and compares these measures across distributions. The authors prove SOL is a metric when the transformer is injective and demonstrate its effectiveness in detecting distributional failures and recovering expected model trends in experiments on OpenWebText models.
In time-series forecasting tasks, the use of recursive forecasting on high-dimensional data substantially increases the complexity of the prediction process. The main challenge is that long-term dynam...
arXiv:2606. 02117v1 Announce Type: cross Abstract: Probabilistic time series forecasting has attracted increasing attention in financial applications due to the need to quantify risk and uncertainty in future observations.