Time-LLM: Reprogram an LLM for Time Series Forecasting
Discover the architecture of Time-LLM and apply it in a forecasting project with Python
It is not the first time that researchers try to apply natural language processing (NLP) techniques to the field of time series.
For example, the Transformer architecture was a significant milestone in NLP, but its performance in time series forecasting remained average, until PatchTST was proposed.
As you know, large language models (LLMs) are being actively developed and have demonstrated impressive generalization and reasoning capabilities in NLP.
Thus, it is worth exploring the idea of repurposing an LLM for time series forecasting, such that we can benefit from the capabilities of those large pre-trained models.
To that end, Time-LLM was proposed. In the original paper, the researchers propose a framework to reprogram an existing LLM to perform time series forecasting.
In this article, we explore the architecture of Time-LLM and how it can effectively allow an LLM to predict time series data. Then, we implement the model and apply it in a small forecasting project.
For more details, make sure to read the original paper.
