Abdul Fatir Ansari

2024

1. TMLR

   Chronos: Learning the Language of Time Series

   Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Syndar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Hao Wang, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, and Bernie Wang

   Transactions on Machine Learning Research, 2024

   3.5K Github stars and 500M+ Hugging Face model downloads as of Aug 2025

   Abstract arXiv Bib Code

   We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models. Chronos tokenizes time series values using scaling and quantization into a fixed vocabulary and trains existing transformer-based language model architectures on these tokenized time series via the cross-entropy loss. We pretrained Chronos models based on the T5 family (ranging from 20M to 710M parameters) on a large collection of publicly available datasets, complemented by a synthetic dataset that we generated via Gaussian processes to improve generalization. In a comprehensive benchmark consisting of 42 datasets, and comprising both classical local models and deep learning methods, we show that Chronos models: (a) significantly outperform other methods on datasets that were part of the training corpus; and (b) have comparable and occasionally superior zero-shot performance on new datasets, relative to methods that were trained specifically on them. Our results demonstrate that Chronos models can leverage time series data from diverse domains to improve zero-shot accuracy on unseen forecasting tasks, positioning pretrained models as a viable tool to greatly simplify forecasting pipelines.

   @article{ansari2024chronos,
     title = {Chronos: Learning the Language of Time Series},
     author = {Ansari, Abdul Fatir and Stella, Lorenzo and Turkmen, Caner and Zhang, Xiyuan and Mercado, Pedro and Shen, Huibin and Shchur, Oleksandr and Rangapuram, Syama Syndar and Pineda Arango, Sebastian and Kapoor, Shubham and Zschiegner, Jasper and Maddix, Danielle C. and Wang, Hao and Mahoney, Michael W. and Torkkola, Kari and Wilson, Andrew Gordon and Bohlke-Schneider, Michael and Wang, Bernie},
     journal = {Transactions on Machine Learning Research},
     issn = {2835-8856},
     year = {2024},
     url = {https://openreview.net/forum?id=gerNCVqqtR},
     note = {3.5K Github stars and 500M+ Hugging Face model downloads as of Aug 2025}
   }

2023

1. ICML Oral

   Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series

   Abdul Fatir Ansari, Alvin Heng, Andre Lim, and Harold Soh

   In International Conference on Machine Learning, 2023

   Oral Presentation (top 2.4%)

   Abstract arXiv Bib Code

   Learning accurate predictive models of real-world dynamic phenomena (e.g., climate, biological) remains a challenging task. One key issue is that the data generated by both natural and artificial processes often comprise time series that are irregularly sampled and/or contain missing observations. In this work, we propose the Neural Continuous-Discrete State Space Model (NCDSSM) for continuous-time modeling of time series through discrete-time observations. NCDSSM employs auxiliary variables to disentangle recognition from dynamics, thus requiring amortized inference only for the auxiliary variables. Leveraging techniques from continuous-discrete filtering theory, we demonstrate how to perform accurate Bayesian inference for the dynamic states. We propose three flexible parameterizations of the latent dynamics and an efficient training objective that marginalizes the dynamic states during inference. Empirical results on multiple benchmark datasets across various domains show improved imputation and forecasting performance of NCDSSM over existing models.

   @inproceedings{ansari2023neural,
     author = {Ansari, Abdul Fatir and Heng, Alvin and Lim, Andre and Soh, Harold},
     title = {Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series},
     year = {2023},
     booktitle = {International Conference on Machine Learning},
     pubtype = {Oral},
     note = {Oral Presentation (top 2.4%)},
   }

2. NeurIPS

   Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting

   Marcel Kollovieh*, Abdul Fatir Ansari*, Michael Bohlke-Schneider, Jasper Zschiegner, Hao Wang, and Yuyang Wang

   In Neural Information Processing Systems, 2023

   Abstract arXiv Bib

   Diffusion models have achieved state-of-the-art performance in generative modeling tasks across various domains. Prior works on time series diffusion models have primarily focused on developing conditional models tailored to specific forecasting or imputation tasks. In this work, we explore the potential of task-agnostic, unconditional diffusion models for several time series applications. We propose TSDiff, an unconditionally trained diffusion model for time series. Our proposed self-guidance mechanism enables conditioning TSDiff for downstream tasks during inference, without requiring auxiliary networks or altering the training procedure. We demonstrate the effectiveness of our method on three different time series tasks: forecasting, refinement, and synthetic data generation. First, we show that TSDiff is competitive with several task-specific conditional forecasting methods (predict). Second, we leverage the learned implicit probability density of TSDiff to iteratively refine the predictions of base forecasters with reduced computational overhead over reverse diffusion (refine). Notably, the generative performance of the model remains intact – downstream forecasters trained on synthetic samples from TSDiff outperform forecasters that are trained on samples from other state-of-the-art generative time series models, occasionally even outperforming models trained on real data (synthesize).

   @inproceedings{kollovieh2023predict,
     title = {Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting},
     author = {Kollovieh*, Marcel and Ansari*, Abdul Fatir and Bohlke-Schneider, Michael and Zschiegner, Jasper and Wang, Hao and Wang, Yuyang},
     year = {2023},
     booktitle = {Neural Information Processing Systems},
   }

2021

1. ICLR

   Refining Deep Generative Models via Discriminator Gradient Flow

   Abdul Fatir Ansari, Ming Liang Ang, and Harold Soh

   In International Conference on Learning Representations, 2021

   Abstract arXiv Bib Blog Code

   Deep generative modeling has seen impressive advances in recent years, to the point where it is now commonplace to see simulated samples (e.g., images) that closely resemble real-world data. However, generation quality is generally inconsistent for any given model and can vary dramatically between samples. We introduce Discriminator Gradient flow (DGflow), a new technique that improves generated samples via the gradient flow of entropy-regularized f-divergences between the real and the generated data distributions. The gradient flow takes the form of a non-linear Fokker-Plank equation, which can be easily simulated by sampling from the equivalent McKean-Vlasov process. By refining inferior samples, our technique avoids wasteful sample rejection used by previous methods (DRS & MH-GAN). Compared to existing works that focus on specific GAN variants, we show our refinement approach can be applied to GANs with vector-valued critics and even other deep generative models such as VAEs and Normalizing Flows. Empirical results on multiple synthetic, image, and text datasets demonstrate that DGflow leads to significant improvement in the quality of generated samples for a variety of generative models, outperforming the state-of-the-art Discriminator Optimal Transport (DOT) and Discriminator Driven Latent Sampling (DDLS) methods.

   @inproceedings{Ansari2021RefiningDG,
     title = {Refining Deep Generative Models via Discriminator Gradient Flow},
     author = {Ansari, Abdul Fatir and Ang, Ming Liang and Soh, Harold},
     booktitle = {International Conference on Learning Representations},
     year = {2021},
   }