The widespread adoption of scalable mobile sensing has led to large amounts
of time series data for real-world applications. A fundamental application is
multivariate time series forecasting (MTSF), which aims to predict future time
series values based on historical observations. Existing MTSF methods suffer
from limited parameterization and small-scale training data. Recently, Large
language models (LLMs) have been introduced in time series, which achieve
promising forecasting performance but incur heavy computational costs. To solve
these challenges, we propose TimeCMA, an LLM-empowered framework for time
series forecasting with cross-modality alignment. We design a dual-modality
encoding module with two branches, where the time series encoding branch
extracts relatively low-quality yet pure embeddings of time series through an
inverted Transformer. In addition, the LLM-empowered encoding branch wraps the
same time series as prompts to obtain high-quality yet entangled prompt
embeddings via a Pre-trained LLM. Then, we design a cross-modality alignment
module to retrieve high-quality and pure time series embeddings from the prompt
embeddings. Moreover, we develop a time series forecasting module to decode the
aligned embeddings while capturing dependencies among multiple variables for
forecasting. Notably, we tailor the prompt to encode sufficient temporal
information into a last token and design the last token embedding storage to
reduce computational costs. Extensive experiments on real data offer insight
into the accuracy and efficiency of the proposed framework.

基于大规模语言模型和交叉模态对齐，提出了 TimeCMA 框架，用于多变量时间序列预测，实验证明其准确性和高效性。