Time-LLM is a reprogramming framework to repurpose LLMs for general time series forecasting with the backbone language models kept intact.

Jun 22, 2024 · We find that removing the LLM component or replacing it with a basic attention layer does not degrade the forecasting results -- in most cases the results even ...

Our study shows that LLMs excel in predicting time series with clear patterns and trends but face challenges with datasets lacking periodicity.

We propose LLMTime, a method for zero-shot time series forecasting with large language models (LLMs) by encoding numbers as text and sampling possible ...

Mar 5, 2024 · In this article, we explore the architecture of Time-LLM and how it can effectively allow an LLM to predict time series data.

Nov 2, 2023 · By preprocessing data, fine-tuning the model, and generating forecasts, LLMs can capture complex patterns, handle unstructured data, and provide ...

Dec 9, 2023 · "Foundational" time series models that are pretrained with many time series and/or work off the back of an LLM are likely the next big thing in time series ...

May 7, 2024 · In this work, we present Time-LLM, a reprogramming framework to repurpose LLMs for general time series forecasting with the backbone language ...

Feb 8, 2024 · This paper introduces TIME-LLM, a novel framework that adapts pre-trained large language models (LLMs) for time series forecasting without altering the ...