TimeDP: Learning to Generate Multi-Domain Time Series with Domain Prompts

Deep Unsupervised Learning using Nonequilibrium Thermodynamics

Sohl-Dickstein, J.; Weiss, E. A.; Maheswaranathan, N.; and Ganguli, S. 2015 · 2015

Earlier work this paper cites.

Time-series Generative Adversarial Networks

Yoon, J.; Jarrett, D.; and van der Schaar, M. 2019 · 2019

Earlier work this paper cites.

Denoising Diffusion Probabilistic Models

Ho, J.; Jain, A.; and Abbeel, P. 2020 · 2020

Earlier work this paper cites.

Towards realistic market simulations: a generative adversarial networks approach

Coletta, A.; Prata, M.; Conti, M.; Mercanti, E.; Bartolini, N.; Moulin, A.; Vyetrenko, S.; and Balch, T. 2021 · 2021

Earlier work this paper cites.

TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation

Original

Desai, A.; Freeman, C.; Wang, Z.; and Beaver, I. 2021 · 2021

Earlier work this paper cites.

Classifier-Free Diffusion Guidance

Ho, J.; and Salimans, T. 2021 · 2021

Earlier work this paper cites.

Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting

Rasul, K.; Seward, C.; Schuster, I.; and Vollgraf, R. 2021 · 2021

Earlier work this paper cites.

Time-series Generative Adversarial Networks

Jeon, J.; KIM, J.; Song, H.; Cho, S.; and Park, N. 2022 · 2022

Earlier work this paper cites.

On the Constrained Time-Series Generation Problem

Coletta, A.; Gopalakrishnan, S.; Borrajo, D.; and Vyetrenko, S. 2023 · 2023

Cited alongside, same era.

Vector Quantized Time Series Generation with a Bidirectional Prior Model

Lee, D.; Malacarne, S.; and Aune, E. 2023 · 2023

Cited alongside, same era.

Causal Recurrent Variational Autoencoder for Medical Time Series Generation

Li, H.; Yu, S.; and Príncipe, J. C. 2023 · 2023

Cited alongside, same era.

Basisformer: Attention-based time series forecasting with learnable and interpretable basis

Ni, Z.; Yu, H.; Liu, S.; Li, J.; and Lin, W. 2023 · 2023

Cited alongside, same era.

A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Nie, Y.; Nguyen, N. H.; Sinthong, P.; and Kalagnanam, J. 2023 · 2023

Cited alongside, same era.

Non-autoregressive conditional diffusion models for time series prediction

UniTS: Building a Unified Time Series Model

Original

Gao, S.; Koker, T.; Queen, O.; Hartvigsen, T.; Tsiligkaridis, T.; and Zitnik, M. 2024 · 2024

Later among the works it cites.

Controllable Financial Market Generation with Diffusion Guided Meta Agent

Original

Huang, Y.-H.; Xu, C.; Liu, Y.; Liu, W.; Li, W.-J.; and Bian, J. 2024 · 2024

Later among the works it cites.

Empowering Time Series Analysis with Large Language Models: A Survey

Jiang, Y.; Pan, Z.; Zhang, X.; Garg, S.; Schneider, A.; Nevmyvaka, Y.; and Song, D. 2024 · 2024

Later among the works it cites.

Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

Jin, M.; Wang, S.; Ma, L.; Chu, Z.; Zhang, J. Y.; Shi, X.; Chen, P.-Y.; Liang, Y.; Li, Y.-F.; Pan, S.; et al. 2024 · 2024

Later among the works it cites.

United We Pretrain, Divided We Fail! Representation Learning for Time Series by Pretraining on 75 Datasets at Once

Original