**Paper:** TabSTAR: A Foundation Tabular Model With Semantically Target-Aware Representations > While deep learning has achieved remarkable success across many domains, it > has historically underperformed on tabular learning tasks, which remain > dominated by gradient boosting decision trees (GBDTs). However, recent > advancements are paving the way for Tabular Foundation Models, which can > leverage real-world knowledge and generalize across diverse datasets, > particularly when the data contains free-text. Although incorporating language > model capabilities into tabular tasks has been explored, most existing methods > utilize static, target-agnostic textual representations, limiting their > effectiveness. We introduce TabSTAR: a Foundation Tabular Model with > Semantically Target-Aware Representations. TabSTAR is designed to enable > transfer learning on tabular data with textual features, with an architecture > free of dataset-specific parameters. It unfreezes a pretrained text encoder and > takes as input target tokens, which provide the model with the context needed > to learn task-specific embeddings. TabSTAR achieves state-of-the-art > performance for both medium- and large-sized datasets across known benchmarks > of classification tasks with text features, and its pretraining phase exhibits > scaling laws in the number of datasets, offering a pathway for further > performance improvements.
🚀 **Update Feb 14, 2025**: Chronos-Bolt models are now available on Amazon SageMaker JumpStart! Check out the tutorial notebook to learn how to deploy Chronos endpoints for production use in a few lines of code. Chronos-Bolt is a family of pretrained time series forecasting models which can be used for zero-shot forecasting. It is based on the T5 encoder-decoder architecture and has been trained on nearly 100 billion time series observations. It chunks the historical time series context into patches of multiple observations, which are then input into the encoder. The decoder then uses these representations to directly generate quantile forecasts across multiple future steps—a method known as direct multi-step forecasting. Chronos-Bolt models are up to 250 times faster and 20 times more memory-efficient than the original Chronos models of the same size. The following plot compares the inference time of Chronos-Bolt against the original Chronos models for forecasting 1024 time series with a context length of 512 observations and a prediction horizon of 64 steps.
🚀 **Update Feb 14, 2025**: Chronos-Bolt models are now available on Amazon SageMaker JumpStart! Check out the tutorial notebook to learn how to deploy Chronos endpoints for production use in a few lines of code. Chronos-Bolt is a family of pretrained time series forecasting models which can be used for zero-shot forecasting. It is based on the T5 encoder-decoder architecture and has been trained on nearly 100 billion time series observations. It chunks the historical time series context into patches of multiple observations, which are then input into the encoder. The decoder then uses these representations to directly generate quantile forecasts across multiple future steps—a method known as direct multi-step forecasting. Chronos-Bolt models are up to 250 times faster and 20 times more memory-efficient than the original Chronos models of the same size. The following plot compares the inference time of Chronos-Bolt against the original Chronos models for forecasting 1024 time series with a context length of 512 observations and a prediction horizon of 64 steps.
🚀 **Update Feb 14, 2025**: Chronos-Bolt & original Chronos models are now available on Amazon SageMaker JumpStart! Check out the tutorial notebook to learn how to deploy Chronos endpoints for production use in a few lines of code. 🚀 **Update Nov 27, 2024**: We have released Chronos-Bolt⚡️ models that are more accurate (5% lower error), up to 250 times faster and 20 times more memory-efficient than the original Chronos models of the same size. Check out the new models here. Chronos is a family of **pretrained time series forecasting models** based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as synthetic data generated using Gaussian processes.
🚀 **Update Feb 14, 2025**: Chronos-Bolt & original Chronos models are now available on Amazon SageMaker JumpStart! Check out the tutorial notebook to learn how to deploy Chronos endpoints for production use in a few lines of code. 🚀 **Update Nov 27, 2024**: We have released Chronos-Bolt⚡️ models that are more accurate (5% lower error), up to 250 times faster and 20 times more memory-efficient than the original Chronos models of the same size. Check out the new models here. Chronos is a family of **pretrained time series forecasting models** based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as synthetic data generated using Gaussian processes.
🚀 **Update Feb 14, 2025**: Chronos-Bolt & original Chronos models are now available on Amazon SageMaker JumpStart! Check out the tutorial notebook to learn how to deploy Chronos endpoints for production use in a few lines of code. 🚀 **Update Nov 27, 2024**: We have released Chronos-Bolt⚡️ models that are more accurate (5% lower error), up to 250 times faster and 20 times more memory-efficient than the original Chronos models of the same size. Check out the new models here. Chronos is a family of **pretrained time series forecasting models** based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as synthetic data generated using Gaussian processes.
🚀 **Update Feb 14, 2025**: Chronos-Bolt & original Chronos models are now available on Amazon SageMaker JumpStart! Check out the tutorial notebook to learn how to deploy Chronos endpoints for production use in a few lines of code. 🚀 **Update Nov 27, 2024**: We have released Chronos-Bolt⚡️ models that are more accurate (5% lower error), up to 250 times faster and 20 times more memory-efficient than the original Chronos models of the same size. Check out the new models here. Chronos is a family of **pretrained time series forecasting models** based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as synthetic data generated using Gaussian processes.
This is a variant of the Chronos-2 model which has _only_ been trained on synthetic univariate and multivariate data. For usage and details on the Chronos-2 model, please refer to https://huggingface.co/autogluon/chronos-2. If you find Chronos-2 useful for your research, please consider citing the associated paper:
🚀 **Update Feb 14, 2025**: Chronos-Bolt models are now available on Amazon SageMaker JumpStart! Check out the tutorial notebook to learn how to deploy Chronos endpoints for production use in a few lines of code. Chronos-Bolt is a family of pretrained time series forecasting models which can be used for zero-shot forecasting. It is based on the T5 encoder-decoder architecture and has been trained on nearly 100 billion time series observations. It chunks the historical time series context into patches of multiple observations, which are then input into the encoder. The decoder then uses these representations to directly generate quantile forecasts across multiple future steps—a method known as direct multi-step forecasting. Chronos-Bolt models are **more accurate**, up to **250 times faster** and **20 times more memory-efficient** than the original Chronos models of the same size. The following plot compares the inference time of Chronos-Bolt against the original Chronos models for forecasting 1024 time series with a context length of 512 observations and a prediction horizon of 64 steps.
🚀 **Update Feb 14, 2025**: Chronos-Bolt & original Chronos models are now available on Amazon SageMaker JumpStart! Check out the tutorial notebook to learn how to deploy Chronos endpoints for production use in a few lines of code. 🚀 **Update Nov 27, 2024**: We have released Chronos-Bolt⚡️ models that are more accurate (5% lower error), up to 250 times faster and 20 times more memory-efficient than the original Chronos models of the same size. Check out the new models here. Chronos is a family of **pretrained time series forecasting models** based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as synthetic data generated using Gaussian processes.
🚀 **Update Feb 14, 2025**: Chronos-Bolt & original Chronos models are now available on Amazon SageMaker JumpStart! Check out the tutorial notebook to learn how to deploy Chronos endpoints for production use in a few lines of code. 🚀 **Update Nov 27, 2024**: We have released Chronos-Bolt⚡️ models that are more accurate (5% lower error), up to 250 times faster and 20 times more memory-efficient than the original Chronos models of the same size. Check out the new models here. Chronos is a family of **pretrained time series forecasting models** based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as synthetic data generated using Gaussian processes.
🚀 **Update Feb 14, 2025**: Chronos-Bolt & original Chronos models are now available on Amazon SageMaker JumpStart! Check out the tutorial notebook to learn how to deploy Chronos endpoints for production use in a few lines of code. 🚀 **Update Nov 27, 2024**: We have released Chronos-Bolt⚡️ models that are more accurate (5% lower error), up to 250 times faster and 20 times more memory-efficient than the original Chronos models of the same size. Check out the new models here. Chronos is a family of **pretrained time series forecasting models** based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as synthetic data generated using Gaussian processes.
Mitra classifier is a tabular foundation model that is pre-trained on purely synthetic datasets sampled from a mix of random classifiers. Mitra is based on a 12-layer Transformer of 72 M parameters, pre-trained by incorporating an in-context learning paradigm. Amazon Science blog: Mitra: Mixed synthetic priors for enhancing tabular foundation models
Mitra-v2 classifier is a tabular foundation model that is pre-trained on purely synthetic datasets sampled from a mix of random classifiers, including the new Hybrid SCM prior. It is the second generation of the Mitra classifier (autogluon/mitra-classifier), pre-trained with a 10x longer context, three times as many features, and an improved optimizer. On the TabArena and TALENT benchmarks it delivers state-of-the-art accuracy at the level of TabFM and EXAONE Tabular, while surpassing TabPFN-3 by a wide margin. The regression model is at autogluon/mitra-regressor-2, and the inference and fine-tuning code with our evaluation results is at autogluon/mitra-finetune. Mitra-v2 is based on a 12-layer 2D Transformer of 75.7 M parameters (attention across rows and across columns), pre-trained by incorporating an in-context learning paradigm. The architecture is unchanged from Mitra-v1; the gains come from the scaled-up synthetic pre-training distribution and the optimizer. A minimal example showing how to fine-tune and predict with the Mitra-v2 classifier using the same recipe as our reported results (50-step fine-tuning with 8-fold bagging). The recipe fine-tunes and bags eight copies of the model and requires a CUDA GPU; each `predict_proba` or `predict` call runs one bagged fine-tune:
Mitra regressor is a tabular foundation model that is pre-trained on purely synthetic datasets sampled from a mix of random regressors. Mitra is based on a 12-layer Transformer of 72 M parameters, pre-trained by incorporating an in-context learning paradigm. Amazon Science blog: Mitra: Mixed synthetic priors for enhancing tabular foundation models
Mitra-v2 regressor is a tabular foundation model that is pre-trained on purely synthetic datasets sampled from a mix of random regressors, including the new Hybrid SCM prior. It is the second generation of the Mitra regressor (autogluon/mitra-regressor), pre-trained with a 10x longer context, three times as many features, and an improved optimizer, and it replaces the scalar mean-squared-error head of Mitra-v1 with a 1,000-bin distributional head. On the TabArena and TALENT benchmarks it delivers state-of-the-art accuracy at the level of TabFM and EXAONE Tabular, while surpassing TabPFN-3 by a wide margin. The classification model is at autogluon/mitra-classifier-2, and the inference and fine-tuning code with our evaluation results is at autogluon/mitra-finetune. Mitra-v2 is based on a 12-layer 2D Transformer of 76.7 M parameters (attention across rows and across columns), pre-trained by incorporating an in-context learning paradigm. Regression is cast as classification over 1,000 target bins: the model predicts a distribution over bins and the point prediction is the mean of that distribution. Apart from this head the architecture is unchanged from Mitra-v1. To use Mitra-v2 regressor, install AutoGluon and the `mitra-finetune` package by running:
MOMENT is a family of foundation models for general-purpose time-series analysis. The models in this family (1) serve as a building block for diverse **time-series analysis tasks** (e.g., forecasting, classification, anomaly detection, and imputation, etc.), (2) are effective **out-of-the-box**, i.e., with no (or few) task-specific exemplars (enabling e.g., zero-shot forecasting, few-shot classification, etc.), and (3) are **tunable** using in-distribution and task-specific data to improve performance. For details on MOMENT models, training data, and experimental results, please refer to the paper MOMENT: A Family of Open Time-series Foundation Models. Here is the list of tutorials and reproducibile experiments to get started with MOMENT for various tasks: - Forecasting - Classification - Anomaly Detection - Imputation - Representation Learning - Real-world Electrocardiogram (ECG) Case Study -- This tutorial also shows how to fine-tune MOMENT for a real-world ECG classification problem, performing training and inference on multiple GPUs and parameter efficient fine-tuning (PEFT).
MOMENT is a family of foundation models for general-purpose time-series analysis. The models in this family (1) serve as a building block for diverse **time-series analysis tasks** (e.g., forecasting, classification, anomaly detection, and imputation, etc.), (2) are effective **out-of-the-box**, i.e., with no (or few) task-specific exemplars (enabling e.g., zero-shot forecasting, few-shot classification, etc.), and (3) are **tunable** using in-distribution and task-specific data to improve performance. For details on MOMENT models, training data, and experimental results, please refer to the paper MOMENT: A Family of Open Time-series Foundation Models. Here is the list of tutorials and reproducibile experiments to get started with MOMENT for various tasks: - Forecasting - Classification - Anomaly Detection - Imputation - Representation Learning - Real-world Electrocardiogram (ECG) Case Study -- This tutorial also shows how to fine-tune MOMENT for a real-world ECG classification problem, performing training and inference on multiple GPUs and parameter efficient fine-tuning (PEFT).
MOMENT is a family of foundation models for general-purpose time-series analysis. The models in this family (1) serve as a building block for diverse **time-series analysis tasks** (e.g., forecasting, classification, anomaly detection, and imputation, etc.), (2) are effective **out-of-the-box**, i.e., with no (or few) task-specific exemplars (enabling e.g., zero-shot forecasting, few-shot classification, etc.), and (3) are **tunable** using in-distribution and task-specific data to improve performance. For details on MOMENT models, training data, and experimental results, please refer to the paper MOMENT: A Family of Open Time-series Foundation Models. Here is the list of tutorials and reproducibile experiments to get started with MOMENT for various tasks: - Forecasting - Classification - Anomaly Detection - Imputation - Representation Learning - Real-world Electrocardiogram (ECG) Case Study -- This tutorial also shows how to fine-tune MOMENT for a real-world ECG classification problem, performing training and inference on multiple GPUs and parameter efficient fine-tuning (PEFT).
