Cover Image

SSD-TS: Exploring the Potential of Linear State Space Models for Diffusion Models in Time Series Imputation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Gao, Hongfan, Shen, Wangmeng, Qiu, Xiangfei, Xu, Ronghui, Hu, Jilin, Yang, Bin
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting
by: Wu, Xingjian, et al.
Published: (2025)

MM-Path: Multi-modal, Multi-granularity Path Representation Learning -- Extended Version
by: Xu, Ronghui, et al.
Published: (2024)

GCGNet: Graph-Consistent Generative Network for Time Series Forecasting with Exogenous Variables
by: Li, Zhengyu, et al.
Published: (2026)

CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching
by: Wu, Xingjian, et al.
Published: (2024)

Bridging Time and Frequency: A Joint Modeling Framework for Irregular Multivariate Time Series Forecasting
by: Qiu, Xiangfei, et al.
Published: (2026)

Enhancing Time Series Forecasting through Selective Representation Spaces: A Patch Perspective
by: Wu, Xingjian, et al.
Published: (2025)

FLAME: Flow Enhanced Legendre Memory Models for General Time Series Forecasting
by: Wu, Xingjian, et al.
Published: (2025)

Latent Space Score-based Diffusion Model for Probabilistic Multivariate Time Series Imputation
by: Liang, Guojun, et al.
Published: (2024)

DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting
by: Qiu, Xiangfei, et al.
Published: (2024)

DAG: A Dual Correlation Network for Time Series Forecasting with Exogenous Variables
by: Qiu, Xiangfei, et al.
Published: (2025)

TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
by: Qiu, Xiangfei, et al.
Published: (2024)

MambaTS: Improved Selective State Space Models for Long-term Time Series Forecasting
by: Cai, Xiuding, et al.
Published: (2024)

SEER: Transformer-based Robust Time Series Forecasting via Automated Patch Enhancement and Replacement
by: Qiu, Xiangfei, et al.
Published: (2026)

Task-Aware Mixture-of-Experts for Time Series Analysis
by: Wu, Xingjian, et al.
Published: (2025)

MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast
by: Hu, Shiyan, et al.
Published: (2024)

Spatial-Temporal Feedback Diffusion Guidance for Controlled Traffic Imputation
by: Mao, Xiaowei, et al.
Published: (2026)

DBLoss: Decomposition-based Loss Function for Time Series Forecasting
by: Qiu, Xiangfei, et al.
Published: (2025)

MTSCI: A Conditional Diffusion Model for Multivariate Time Series Consistent Imputation
by: Zhou, Jianping, et al.
Published: (2024)

Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baseline
by: Liu, Xvyuan, et al.
Published: (2025)

Diffusion-TS: Interpretable Diffusion for General Time Series Generation
by: Yuan, Xinyu, et al.
Published: (2024)

UniTS: A Unified Multi-Task Time Series Model
by: Gao, Shanghua, et al.
Published: (2024)

ASTGI: Adaptive Spatio-Temporal Graph Interactions for Irregular Multivariate Time Series Forecasting
by: Liu, Xvyuan, et al.
Published: (2025)

TimeART: Towards Agentic Time Series Reasoning via Tool-Augmentation
by: Wu, Xingjian, et al.
Published: (2026)

STDiff: A State Transition Diffusion Framework for Time Series Imputation in Industrial Systems
by: Simethy, Gary, et al.
Published: (2025)

Baguan-TS: A Sequence-Native In-Context Learning Model for Time Series Forecasting with Covariates
by: Yang, Linxiao, et al.
Published: (2026)

NuwaTS: a Foundation Model Mending Every Incomplete Time Series
by: Cheng, Jinguo, et al.
Published: (2024)

ST-EVO: Towards Generative Spatio-Temporal Evolution of Multi-Agent Communication Topologies
by: Wu, Xingjian, et al.
Published: (2026)

TS-LIF: A Temporal Segment Spiking Neuron Network for Time Series Forecasting
by: Feng, Shibo, et al.
Published: (2025)

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

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

TS-Reasoner: Aligning Time Series Foundation Models with LLM Reasoning
by: Yu, Fangxu, et al.
Published: (2025)

TS-Memory: Plug-and-Play Memory for Time Series Foundation Models
by: Lyu, Sisuo, et al.
Published: (2026)

Glocal Information Bottleneck for Time Series Imputation
by: Yang, Jie, et al.
Published: (2025)

FADTI: Fourier and Attention Driven Diffusion for Multivariate Time Series Imputation
by: Li, Runze, et al.
Published: (2025)

A Comprehensive Survey of Deep Learning for Multivariate Time Series Forecasting: A Channel Strategy Perspective
by: Qiu, Xiangfei, et al.
Published: (2025)

An Encode-then-Decompose Approach to Unsupervised Time Series Anomaly Detection on Contaminated Training Data--Extended Version
by: Zhang, Buang, et al.
Published: (2025)

Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations
by: Naour, Etienne Le, et al.
Published: (2023)

FlowTS: Time Series Generation via Rectified Flow
by: Hu, Yang, et al.
Published: (2024)

Joint Selective State Space Model and Detrending for Robust Time Series Anomaly Detection
by: Chen, Junqi, et al.
Published: (2024)

Series-to-Series Diffusion Bridge Model
by: Yang, Hao, et al.
Published: (2024)