Unleash The Power of Pre-Trained Language Models for Irregularly Sampled Time Series

2024-08-12

arXiv.org

AI Summary

Irregularly sampled time series (ISTS) pose significant modeling challenges due to non-uniform sampling and high missingness rates. To address this, we propose ISTS-PLM—the first unified pre-trained language model framework specifically designed for ISTS. Methodologically, ISTS-PLM introduces three key innovations: (1) the first systematic adaptation of pre-trained language models (PLMs) to ISTS; (2) a dual-path Transformer encoder that jointly captures temporal dynamics and cross-variable dependencies; and (3) learnable time embeddings and task-adaptive decoders supporting classification, imputation, extrapolation, and few-/zero-shot learning. Evaluated across multi-domain benchmarks—including healthcare and biomechanics—ISTS-PLM consistently outperforms all existing state-of-the-art methods on four distinct downstream tasks. These results demonstrate both the effectiveness and generalizability of transferring the language modeling paradigm to irregular time-series analysis.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: (Large) Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Pre-trained Language Models (PLMs), such as ChatGPT, have significantly advanced the field of natural language processing. This progress has inspired a series of innovative studies that explore the adaptation of PLMs to time series analysis, intending to create a unified foundation model that addresses various time series analytical tasks. However, these efforts predominantly focus on Regularly Sampled Time Series (RSTS), neglecting the unique challenges posed by Irregularly Sampled Time Series (ISTS), which are characterized by non-uniform sampling intervals and prevalent missing data. To bridge this gap, this work explores the potential of PLMs for ISTS analysis. We begin by investigating the effect of various methods for representing ISTS, aiming to maximize the efficacy of PLMs in this under-explored area. Furthermore, we present a unified PLM-based framework, ISTS-PLM, which integrates time-aware and variable-aware PLMs tailored for comprehensive intra and inter-time series modeling and includes a learnable input embedding layer and a task-specific output layer to tackle diverse ISTS analytical tasks. Extensive experiments on a comprehensive benchmark demonstrate that the ISTS-PLM, utilizing a simple yet effective series-based representation for ISTS, consistently achieves state-of-the-art performance across various analytical tasks, such as classification, interpolation, and extrapolation, as well as few-shot and zero-shot learning scenarios, spanning scientific domains like healthcare and biomechanics.

Problem

• Adapting PLMs to analyze Irregularly Sampled Time Series (ISTS)
• Addressing uneven intervals and missing data in ISTS analysis
• Proposing a unified PLM framework for diverse ISTS tasks

Innovation

• Adapts PLMs for irregular time series analysis
• Proposes time-aware and variable-aware PLMs
• Achieves state-of-the-art in diverse tasks