Time-LLM: Transforming Time-Series Forecasting with Large Language Models
The Time-LLM framework repurposes pre-trained large language models (LLMs) like Mistral and LLaMA for time-series forecasting tasks, translating numerical data into a format that LLMs can comprehend. It consists of four key components: an input embedding layer that converts raw data into embeddings, a reprogramming module mapping these embeddings to text prototypes, a frozen pre-trained LLM layer processing this data without fine-tuning, and an output projection layer translating LLM outputs back into time-series predictions.
This architecture minimizes computational costs and leverages few-shot learning, showcasing strong performance compared to specialized models. However, it requires significant GPU memory and may lack interpretability. Ideal for scenarios with limited training data, Time-LLM seamlessly integrates time-series analysis with conversational AI, exemplifying how LLMs can yield actionable insights from complex datasets and redefining interactions with data-driven systems.
