In recent years, researchers have increasingly applied natural language processing (NLP) techniques to the field of time series. Large language models (LLMs) have made a significant impact in NLP with their reasoning capabilities and generalization. Efforts have started to repurpose these LLMs for time series forecasting, leading to the creation of Time-LLM.
Time-LLM, rather than being a model with a specific architecture, should be seen as a framework. Its purpose is the reprogramming of an existing LLM to forecast time series—an execution quite dissimilar to the usual fine-tuning of LLMs. What we do is train the LLM to accept an input sequence of timestamps and output forecasts spanning a set time horizon. This operation leaves the LLM untouched.
Although NLP has had significant milestones, such as the Transformer architecture, its efficacy in time series forecasting remained moderate until the introduction of PatchTST. However, Time-LLM has demonstrated its proficiency in enlisting an LLM to predict time series data.
So, how does Time-LLM work? It begins by tokenizing the input sequence of the time series with a customized layer of patch embedding. After that, these patches are put through to the LLM. The resulting structure, as depicted in the Time-LLM paper authored by M. Jin and team, shows how Time-LLM reprograms an “embedding-visible” language foundation model, such as LLaMA or GPT-2.
Successful forecasting of time series by reprogramming LLMs has important implications. It opens an avenue for utilizing enormously trained existing models for fields beyond their original purpose. Therefore, there’s a lot to look forward to in using Time-LLM and repurposing LLMs.
While the steps may seem straightforward, a deeper understanding of the techniques entails a perusal of the original paper on Time-LLM by M. Jin and his team. These pretty much cover the basics, but in-depth insights could only be gleaned by getting into the thick of Time-LLM’s workings.
What’s obvious, though, is that repurposing an LLM for time series forecasting harnesses the power of large pre-existing models—an approach that bears watching in the future. The fact that an LLM could be repurposed to accept a completely different input and produce the desired output not only tells us of its versatility but also signals boundless possibilities in the field of artificial intelligence.
Foremost, of course, is the significant reduction of work and resources, given that researchers no longer have to build a model from scratch. Another advantageous aspect is the already proven capability of LLMs, which have demonstrated impressive results in NLP. With the arrival and successful application of Time-LLM, it would not be far-fetched to think of other potential applications.
In this respect, we have started to explore Time-LLM for time series forecasting and to apply it to a small forecasting project using Python. This foray into Time-LLM’s potential could lead to substantial real-world applications in various industries that depend on time series data, such as the financial sector, health care, and manufacturing, to mention a few. Thus, Time-LLM represents a compelling step forward in the field of AI and NLP.
