:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Lan, Shaocheng, Gu, Shuqi, Xiong, Zhangzhi, Ren, Kan
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2603.04767
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Exploring the Potential of Probabilistic Transformer for Time Series Modeling: A Report on the ST-PT Framework
by: Xiong, Zhangzhi, et al.
Published: (2026)

Kairos: Toward Adaptive and Parameter-Efficient Time Series Foundation Models
by: Feng, Kun, et al.
Published: (2025)

What if Tomorrow is the World Cup Final? Counterfactual Time Series Forecasting with Textual Conditions
by: Gu, Shuqi, et al.
Published: (2026)

TSFM-Bench: A Comprehensive and Unified Benchmark of Foundation Models for Time Series Forecasting
by: Li, Zhe, et al.
Published: (2024)

Learning to Select In-Context Demonstration Preferred by Large Language Model
by: Zhang, Zheng, et al.
Published: (2025)

OrionBench: Benchmarking Time Series Generative Models in the Service of the End-User
by: Alnegheimish, Sarah, et al.
Published: (2023)

TSI-Bench: Benchmarking Time Series Imputation
by: Du, Wenjie, et al.
Published: (2024)

TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Models
by: Si, Haotian, et al.
Published: (2024)

QuitoBench: A High-Quality Open Time Series Forecasting Benchmark
by: Xue, Siqiao, et al.
Published: (2026)

Coupling Models for One-Step Discrete Generation
by: Peng, Fred Zhangzhi, et al.
Published: (2026)

GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation
by: Aksu, Taha, et al.
Published: (2024)

TAB: Unified Benchmarking of Time Series Anomaly Detection Methods
by: Qiu, Xiangfei, et al.
Published: (2025)

BEDTime: A Unified Benchmark for Automatically Describing Time Series
by: Sen, Medhasweta, et al.
Published: (2025)

Benchmarking Counterfactual Interpretability in Deep Learning Models for Time Series Classification
by: Kan, Ziwen, et al.
Published: (2024)

Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation
by: Jing, Baoyu, et al.
Published: (2024)

PYRREGULAR: A Unified Framework for Irregular Time Series, with Classification Benchmarks
by: Spinnato, Francesco, et al.
Published: (2025)

GC-Bench: An Open and Unified Benchmark for Graph Condensation
by: Sun, Qingyun, et al.
Published: (2024)

Time-SSM: Simplifying and Unifying State Space Models for Time Series Forecasting
by: Hu, Jiaxi, et al.
Published: (2024)

Channel-aware Contrastive Conditional Diffusion for Multivariate Probabilistic Time Series Forecasting
by: Li, Siyang, et al.
Published: (2024)

DynaConF: Dynamic Forecasting of Non-Stationary Time Series
by: Liu, Siqi, et al.
Published: (2022)

Unified Training of Universal Time Series Forecasting Transformers
by: Woo, Gerald, et al.
Published: (2024)

Time Weaver: A Conditional Time Series Generation Model
by: Narasimhan, Sai Shankar, et al.
Published: (2024)

CityTrajBench: A Unified Benchmark for City-Scale Vehicle Trajectory Generation
by: Zhu, Shibo, et al.
Published: (2026)

Conditional Sig-Wasserstein GANs for Time Series Generation
by: Liao, Shujian, et al.
Published: (2020)

T2I-ConBench: Text-to-Image Benchmark for Continual Post-training
by: Huang, Zhehao, et al.
Published: (2025)

ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling
by: Chen, Yuqi, et al.
Published: (2024)

TimeAutoDiff: A Unified Framework for Generation, Imputation, Forecasting, and Time-Varying Metadata Conditioning of Heterogeneous Time Series Tabular Data
by: Suh, Namjoon, et al.
Published: (2024)

A Unified Contrastive-Generative Framework for Time Series Classification
by: Liu, Ziyu, et al.
Published: (2025)

Time Series Generation Under Data Scarcity: A Unified Generative Modeling Approach
by: Gonen, Tal, et al.
Published: (2025)

CoIFNet: A Unified Framework for Multivariate Time Series Forecasting with Missing Values
by: Tang, Kai, et al.
Published: (2025)

Unified Long-Term Time-Series Forecasting Benchmark
by: Cyranka, Jacek, et al.
Published: (2023)

TempusBench: An Evaluation Framework for Time-Series Forecasting
by: Goktas, Denizalp, et al.
Published: (2026)

Towards Time Series Generation Conditioned on Unstructured Natural Language
by: Woo, Jaeyun, et al.
Published: (2025)

Conditionally Whitened Generative Models for Probabilistic Time Series Forecasting
by: Yang, Yanfeng, et al.
Published: (2025)

NeuralBench: A Unifying Framework to Benchmark NeuroAI Models
by: Banville, Hubert, et al.
Published: (2026)

Automated Contrastive Learning Strategy Search for Time Series
by: Jing, Baoyu, et al.
Published: (2024)

Impermanent: A Live Benchmark for Temporal Generalization in Time Series Forecasting
by: Garza, Azul, et al.
Published: (2026)

TimeOmni-VL: Unified Models for Time Series Understanding and Generation
by: Guan, Tong, et al.
Published: (2026)

Don't Retrain, Align: Adapting Autoregressive LMs to Diffusion LMs via Representation Alignment
by: Peng, Fred Zhangzhi, et al.
Published: (2026)

TemporalBench: A Benchmark for Evaluating LLM-Based Agents on Contextual and Event-Informed Time Series Tasks
by: Weng, Muyan, et al.
Published: (2026)