Time series forecasting is important in many sectors, including finance, weather, and health, as it enables predictions based on past patterns. While traditional methods like ARIMA and exponential smoothing are popular, they often fall short in complex and large-scale forecasting tasks. Herein lies the role of natural language processing (NLP), and more specifically large language models (LLMs) like Time-LLM.
Although past attempts at incorporating Transformer architecture (an NLP technique) into time series forecasting yielded average results, Time-LLM is different. Time-LLM utilises a unique reprogramming approach to leverage the strengths of existing LLMs.
LLMs, especially in their pre-trained variant, have proven successful in tasks that involve natural language understanding and reasoning. Given this, researchers have proposed the idea of using these models for time series forecasting. This idea was operationalised in the form of Time-LLM.
Time-LLM was proposed as a framework – an architectural structure – rather than a standalone model. This framework provides a way to repurpose pre-existing LLMs for the purpose of time series forecasting. The end goal is to train these LLMs to accept a sequence of time steps, process them, and eventually output forecasts.
The functioning of Time-LLM is as follows. The input time series sequence is tokenised with a custom patch embedding layer. These patches, or segments of the series, are processed by the LLM. Consequently, the Time-LLM architecture framework can make the LLM learn to produce time series forecasts.
However, it’s important to note that although Time-LLM leverages an existing LLM, it is not a fine-tuning of the LLM per se. Rather, it’s seen as teaching the LLM a new capacity: the ability to process time series data.
In conclusion, Time-LLM presents a novel approach to time series forecasting by utilizing the powerful capabilities of LLMs. It serves as a bridge that connects natural language processing with time series forecasting, further expanding the array of tools available to data scientists and researchers in the field. This methodology still needs exploration and testing but it surely shows the possibility of using NLP techniques for time series forecast.
