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.
Let’s get started!
Time-LLM is to be considered more as a framework than an actual model with a specific architecture.
The general structure of Time-LLM is shown below.
The entire idea behind Time-LLM is to reprogram an embedding-visible language foundation model, like LLaMA or GPT-2.
Note that this is different from fine-tuning the LLM. Instead, we teach the LLM to take an input sequence of time steps and output forecasts over a certain horizon. This means that the LLM itself stays unchanged.
At a high level, Time-LLM starts by tokenizing the input time series sequence with a customized patch embedding layer. These patches are then sent through…
