Adapting Large Language Models for Time Series Modeling via a Novel Parameter-efficient Adaptation Method

Understand

Time series modeling holds significant importance in many real-world applications and has been extensively studied.

• While pre-trained foundation models have made impressive strides in the fields of natural language processing (NLP) and computer vision (CV), their development in time series domains has been constrained by data sparsity.
• A series of recent studies have demonstrated that large language models (LLMs) possess robust pattern recognition and reasoning abilities over complex sequences of tokens.
• However, the current literature have yet striked a high-quality balance between (a) effectively aligning the time series and natural language modalities, and (b) keeping the inference efficiency.