Fetching the paper…
Reading the bibliography…
Time-series Generation (TSG) is a prominent research area with broad applications in simulations, data augmentation, and counterfactual analysis.
A mean squared error criterion for time series data windows
Hurvich, C. M · 1988
Earlier work this paper cites.
Stl: A seasonal-trend decomposition
Cleveland, R. B., Cleveland, W. S., McRae, J. E., Terpenning, I., et al · 1990
Earlier work this paper cites.
Kolmogorov–smirnov test: Overview
Berger, V. W. and Zhou, Y · 2014
Earlier work this paper cites.
The m4 competition: Results, findings, conclusion and way forward
Makridakis, S., Spiliotis, E., and Assimakopoulos, V · 2018
Earlier work this paper cites.
Statistical aspects of wasserstein distances
Panaretos, V. M. and Zemel, Y · 2019
Earlier work this paper cites.
Time-series generative adversarial networks
Yoon, J., Jarrett, D., and van der Schaar, M · 2019
Earlier work this paper cites.
GluonTS: Probabilistic and Neural Time Series Modeling in Python
Alexandrov, A., Benidis, K., Bohlke-Schneider, M., Flunkert, V., Gasthaus, J., Januschowski, T., Maddix, D. C., Rangapuram, S., Salinas, D., Schulz, J., Stella, L., Türkmen, A. C., and Wang, Y · 2020
Earlier work this paper cites.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Earlier work this paper cites.
N-BEATS: neural basis expansion analysis for interpretable time series forecasting
Oreshkin, B. N., Carpov, D., Chapados, N., and Bengio, Y · 2020
Earlier work this paper cites.
Financial time series forecasting with deep learning : A systematic literature review: 2005-2019
Sezer, O. B., Gudelek, M. U., and Özbayoglu, A. M · 2020
Earlier work this paper cites.
Timevae: A variational auto-encoder for multivariate time series generation
Desai, A., Freeman, C., Wang, Z., and Beaver, I · 2021
Earlier work this paper cites.
Monash time series forecasting archive
Godahewa, R., Bergmeir, C., Webb, G. I., Hyndman, R. J., and Montero-Manso, P · 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
Earlier work this paper cites.
CSDI: conditional score-based diffusion models for probabilistic time series imputation
Tashiro, Y., Song, J., Song, Y., and Ermon, S · 2021
Earlier work this paper cites.
Performing price scenario analysis and stress testing using quantile regression: A case study of the californian electricity market
Westgaard, S., Fleten, S.-E., Negash, A., Botterud, A., Bogaard, K., and Verling, T. H · 2021
Earlier work this paper cites.
Towards neural numeric-to-text generation from temporal personal health data
Harris, J. J. and Zaki, M. J · 2022
Earlier work this paper cites.
Time series analysis and forecasting of air pollutants based on prophet forecasting model in jiangsu province, china
Hasnain, A., Sheng, Y., Hashmi, M. Z., Bhatti, U. A., Hussain, A., Hameed, M., Marjan, S., Bazai, S. U., Hossain, M. A., Sahabuddin, M., et al · 2022
Earlier work this paper cites.
GT-GAN: general purpose time series synthesis with generative adversarial networks
Jeon, J., Kim, J., Song, H., Cho, S., and Park, N · 2022
Earlier work this paper cites.
Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E. H., Le, Q. V., and Zhou, D · 2022
Earlier work this paper cites.
Diffusion-based time series imputation and forecasting with structured state space models
Alcaraz, J. M. L. and Strodthoff, N · 2023
Earlier work this paper cites.
LLM4TS: two-stage fine-tuning for time-series forecasting with pre-trained llms
Chang, C., Peng, W., and Chen, T · 2023
Earlier work this paper cites.
Chatgpt informed graph neural network for stock movement prediction
Chen, Z., Zheng, L. N., Lu, C., Yuan, J., and Zhu, D · 2023
Earlier work this paper cites.
On the constrained time-series generation problem
Coletta, A., Gopalakrishnan, S., Borrajo, D., and Vyetrenko, S · 2023
Cited alongside, same era.
Large language models are zero-shot time series forecasters
Gruver, N., Finzi, M., Qiu, S., and Wilson, A. G · 2023
Cited alongside, same era.
Drafting event schemas using language models
Gunjal, A. and Durrett, G · 2023
Cited alongside, same era.
A prediction model for healthcare time-series data with a mixture of deep mixed effect models using gaussian processes
Hong, J. and Chun, H · 2023
Cited alongside, same era.
TCGAN: convolutional generative adversarial network for time series classification and clustering
Huang, F. and Deng, Y · 2023
Cited alongside, same era.
Time-llm: Time series forecasting by reprogramming large language models
Mg-tsd: Multi-granularity time series diffusion models with guided learning process
Fan, X., Wu, Y., Xu, C., Huang, Y., Liu, W., and Bian, J · 2024
Later among the works it cites.
Connecting large language models with evolutionary algorithms yields powerful prompt optimizers
Guo, Q., Wang, R., Guo, J., Li, B., Song, K., Tan, X., Liu, G., Bian, J., and Yang, Y · 2024
Later among the works it cites.
Invdiff: Invariant guidance for bias mitigation in diffusion models
Hou, M., Wu, Y., Xu, C., Huang, Y.-H., Bai, C., Wu, L., and Bian, J · 2024
Later among the works it cites.
Controllable financial market generation with diffusion guided meta agent
Huang, Y., Xu, C., Liu, Y., Liu, W., Li, W., and Bian, J · 2024
Later among the works it cites.
Predict, refine, synthesize: Self-guiding diffusion models for probabilistic time series forecasting
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jin, M., Wang, S., Ma, L., Chu, Z., Zhang, J. Y., Shi, X., Chen, P., Liang, Y., Li, Y., Pan, S., and Wen, Q · 2023
Cited alongside, same era.
Vector quantized time series generation with a bidirectional prior model
Lee, D., Malacarne, S., and Aune, E · 2023
Cited alongside, same era.
Team: PULSAR at probsum 2023: PULSAR: pre-training with extracted healthcare terms for summarising patients’ problems and data augmentation with black-box large language models
Li, H., Wu, Y., Schlegel, V., Batista-Navarro, R., Nguyen, T., Kashyap, A. R., Zeng, X., Beck, D., Winkler, S., and Nenadic, G · 2023
Cited alongside, same era.
Can chatgpt forecast stock price movements? return predictability and large language models
Lopez-Lira, A. and Tang, Y · 2023
Cited alongside, same era.
Self-refine: Iterative refinement with self-feedback
Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., Gupta, S., Majumder, B. P., Hermann, K., Welleck, S., Yazdanbakhsh, A., and Clark, P · 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
Cited alongside, same era.
PULSAR at mediqa-sum 2023: Large language models augmented by synthetic dialogue convert patient dialogues to medical records
Schlegel, V., Li, H., Wu, Y., Subramanian, A., Nguyen, T., Kashyap, A. R., Beck, D., Zeng, X., Batista-Navarro, R. T., Winkler, S., and Nenadic, G · 2023
Cited alongside, same era.
Kollovieh, M., Ansari, A. F., Bohlke-Schneider, M., Zschiegner, J., Wang, H., and Wang, Y. B · 2024
Later among the works it cites.
Foundation models for time series analysis: A tutorial and survey
Liang, Y., Wen, H., Nie, Y., Jiang, Y., Jin, M., Song, D., Pan, S., and Wen, Q · 2024
Later among the works it cites.
Diffusion models for time-series applications: a survey
Lin, L., Li, Z., Li, R., Li, X., and Gao, J · 2024
Later among the works it cites.
Unitime: A language-empowered unified model for cross-domain time series forecasting
Liu, X., Hu, J., Li, Y., Diao, S., Liang, Y., Hooi, B., and Zimmermann, R · 2024
Later among the works it cites.
Language models still struggle to zero-shot reason about time series
Merrill, M. A., Tan, M., Gupta, V., Hartvigsen, T., and Althoff, T · 2024
Later among the works it cites.
Time weaver: A conditional time series generation model
Narasimhan, S. S., Agarwal, S., Akcin, O., Sanghavi, S., and Chinchali, S. P · 2024
Later among the works it cites.
Timeldm: Latent diffusion model for unconditional time series generation
Qian, J., Xie, B., Wan, B., Li, M., Sun, M., and Chiang, P. Y · 2024
Later among the works it cites.
Multi-resolution diffusion models for time series forecasting
Shen, L., Chen, W., and Kwok, J. T · 2024
Later among the works it cites.
Language models can improve event prediction by few-shot abductive reasoning
Shi, X., Xue, S., Wang, K., Zhou, F., Zhang, J., Zhou, J., Tan, C., and Mei, H · 2024
Later among the works it cites.
Thread detection and response generation using transformers with prompt optimisation
T, K. J., Agarwal, A., Sanjay, S., Sarda, Y., Alex, J. S. R., Gupta, S., Kumar, S., and Kamath, V · 2024
Later among the works it cites.
Unified training of universal time series forecasting transformers
Woo, G., Liu, C., Kumar, A., Xiong, C., Savarese, S., and Sahoo, D · 2024
Later among the works it cites.
Promptcast: A new prompt-based learning paradigm for time series forecasting
Xue, H. and Salim, F. D · 2024
Later among the works it cites.
Diffusion-ts: Interpretable diffusion for general time series generation
Yuan, X. and Qiao, Y · 2024
Later among the works it cites.
Geng: An llm-based generic time series data generation approach for edge intelligence via cross-domain collaboration
Zhou, X., Jia, Q., Hu, Y., Xie, R., Huang, T., and Yu, F. R · 2024
Later among the works it cites.
Deng, B., Xu, C., Li, H., Huang, Y., Hou, M., and Bian, J · 2025
Closest in time.
Timedp: Learning to generate multi-domain time series with domain prompts
Huang, Y.-H., Xu, C., Wu, Y., Li, W.-J., and Bian, J · 2025
Closest in time.
Generating realistic multi-beat ecg signals
Pöhl, P., Schlegel, V., Li, H., and Bharath, A · 2025
Closest in time.