Exploring Chronos: How fundamental AI models are setting new standards for predictive analytics
This post was co-authored with Rafael Guedes.
Time series forecasting has evolved into a fundamental model due to its success in other areas of artificial intelligence (AI). In particular, we have seen the success of such approaches in natural language processing (NLP). The pace of development of basic models is accelerating over time. New, more powerful Large Language Models (LLMs) are released every month. This is not limited to NLP. A similar growth pattern is seen in computer vision. Segmentation models like Meta's Segment Anything Model (SAM) [1] Identify and accurately segment objects in images that are invisible to the naked eye. Multimodal models such as LLaVa [2] or Qwen-VL [3] It can process text and images to answer user questions. A common feature of these models is their ability to perform accurate zero-shot inference. This means it doesn't need to be trained on data to perform well.
It is probably useful at this point to define what the underlying model is and what makes it different from traditional approaches. First, the underlying model (and thus training) is large-scale and provides a broad understanding of the main patterns and important nuances found in the data. Second, it is generic. This means that the base model can perform a variety of tasks without requiring task-specific training. No task-specific training is required, but it can be fine-tuned (also known as transfer learning). They adapt to relatively small datasets and perform better on certain tasks.
Based on the above, why is it so attractive to apply it to time series forecasting? First, we design a basic model in NLP to understand and generate text sequences. Fortunately, time series data is also sequential. The previous point is also consistent with the fact that for both problems, the model needs to automatically extract and learn relevant features from a set of data (temporal dynamics of time series data). Furthermore, the versatility of the underlying models means that they can be adapted to different prediction tasks. This flexibility makes it possible to apply a single powerful model to different domains.