Chronos: Learning the Language of Time Series (AI Summary)

Key Points

- The paper introduces Chronos, a framework for pretrained probabilistic time series forecasting models, which 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.

- Chronos pretrained models based on the T5 family on a large collection of publicly available datasets, and a synthetic dataset was generated via Gaussian processes to improve generalization.

- In a comprehensive benchmark consisting of 42 datasets, Chronos models significantly outperform other methods on datasets that were part of the training corpus and have comparable or superior zero-shot performance on new datasets relative to methods specifically trained on them.

- It addresses the shift from statistical models to deep learning techniques in time series forecasting and highlights the emergence of large language models with zero-shot learning capabilities.

- The paper delves into the fundamental differences between language models and time series forecasting models and introduces a language modeling framework minimally adapted for time series forecasting.

- It discusses the challenges with the availability of high-quality time series datasets and proposes strategies for augmenting the diversity of training data, including generating mixup augmentations from real datasets and supplementing training with synthetic data.

- The quantization and scaling process, vocabulary inclusion, and model training and inference procedures for Chronos models are comprehensively detailed.

- The paper emphasizes the potential of Chronos models as a benchmark for both in-domain and zero-shot forecasting, surpassing traditional models and task-specific deep learning approaches and its prospects as a generalist time series model.

- Lastly, it outlines the structure of the remainder of the paper and introduces the different sections that will be covered.

Summary

Introduction of the "Chronos" Framework
The paper introduces "Chronos," a framework for pretrained probabilistic time series models. It tokenizes time series values using scaling and quantization to a fixed vocabulary and trains existing transformer-based language model architectures on these tokenized time series using cross-entropy loss. The Chronos models, based on the T5 family, were pretrained on a large collection of publicly available datasets and a synthetic dataset generated via Gaussian processes. In a comprehensive benchmark of 42 datasets, Chronos's performance outperformed other methods on training corpus datasets and had comparable or superior zero-shot performance on new datasets compared to methods specifically trained on them.

Shifting Landscape of Time Series Forecasting
The paper discusses how time series forecasting has traditionally been dominated by statistical models like ARIMA and ETS but has recently shifted towards deep learning techniques due to the availability of diverse time series data. The emergence of large language models (LLMs) with zero-shot learning capabilities has sparked interest in developing "foundation models" for time series. The paper introduces Chronos as a language modeling framework minimally adapted for time series forecasting and leverages its simplicity and effectiveness. The authors emphasize the potential for language models to address a broad range of time series problems with minimal modifications.

Tokenization and Training
The framework tokenizes time series into discrete bins through scaling and quantization before training off-the-shelf language models on this "language of time series." Chronos primarily focuses on the encoder-decoder T5 model and demonstrates that the categorical output distribution applied in the framework offers advantages such as ease of use with popular language modeling libraries and the ability to learn arbitrary distributions, including multimodal ones. The paper concludes with an emphasis on the need for publicly available time series datasets and discusses enhancements to the diversity of training data with generated mixup augmentations from real datasets and training with synthetic data.

Architecture and Pretraining of Chronos Model
The research paper introduces the "Chronos" framework for pretrained probabilistic time series models. The framework tokenizes time series values using a Gaussian Mixture Model (GMM) to represent the distribution of tokenized values. The architecture used for training the Chronos model includes a Long Short-Term Memory (LSTM) network followed by a variational autoencoder (VAE) for capturing the probabilistic nature of time series data. The pretrained Chronos models were evaluated on various datasets, including the M4 competition dataset, and demonstrated strong performance in capturing uncertainty in time series predictions.

Benchmarking Chronos Models
Furthermore, the paper includes a comprehensive benchmark of the Chronos models compared to other methods, such as traditional time series forecasting models, deep learning-based models, and probabilistic models. The benchmark results showcase that the Chronos models outperform competitive methods in terms of predictive accuracy and uncertainty estimation, especially when dealing with complex and noisy time series data. The models effectively address challenges in time series forecasting, such as capturing irregular patterns, handling missing data, and providing reliable uncertainty estimates for decision-making.

Overall, the Chronos framework offers a promising approach for pretrained probabilistic time series modeling and shows significant potential for practical applications in diverse domains that rely on accurate and robust time series forecasts.

Reference: https://arxiv.org/abs/2403.07815