Using Pre-trained LLMs for Multivariate Time Series Forecasting
This paper fine-tunes pre-trained language models using novel multivariate patching and weight diagnostics to improve time series demand forecasting.
What it examines
The paper explores using pre-trained LLMs for multivariate time series forecasting. It introduces a novel multivariate patching method to map time series features into the LLM token space and fine-tunes only key parameters (layer norms), aiming to achieve competitive forecasting performance with minimal adjustments.
What it concludes
Results demonstrate that fine-tuning pre-trained LLMs can deliver near state-of-the-art forecasting accuracy. This approach is promising for applications like retail demand forecasting and other time-dependent tasks. Future work should test larger datasets and further refine LLM adaptation to diverse forecasting challenges.
Evidence objects
Researchers unveil a groundbreaking method that converts multivariate numeric data into token embeddings, enabling pre-trained language models like GPT-2, Flan-T5, and MPT-7B to achieve superior forecasting performance with minimal fine-tuning.
key_findings bullet 1 · key_findings · validation V0
Authors introduce an innovative new multivariate patching strategy aggregating time series and static features with diagnostic measures based on Heavy-Tailed Self-Regularization theory, linking LLM spectral properties to enhanced forecast accuracy.
key_findings bullet 2 · key_findings · validation V0
Real product demand experiments reveal efficient, low-overhead training transferring language knowledge to forecasting, though reliance on limited datasets and short training epochs underscores need for further research and robust validation.
key_findings bullet 3 · key_findings · validation V0
This innovative paper pioneers the use of pre-trained large language models for multivariate time series forecasting, employing a novel multivariate patching strategy with minimal fine-tuning restricted exclusively to layer norms. Coupled with heavy-tailed self-regularization diagnostics, the methodology presents fresh, impactful insights and uniquely compelling applications, particularly in advanced financial forecasting.
key_findings bullet 4 · key_findings · validation V0
Raw abstract and provenance
- … -based time series forecasting, using product demand data from a large internet retailer. Our comparison baseline is a variant of an existing production forecasting system. …
Source row: 2114 · abstract type: snippet