Summary of Timecma: Towards Llm-empowered Multivariate Time Series Forecasting Via Cross-modality Alignment, by Chenxi Liu et al.
TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment
by Chenxi Liu, Qianxiong Xu, Hao Miao, Sun Yang, Lingzheng Zhang, Cheng Long, Ziyue Li, Rui Zhao
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel framework called TimeCMA for multivariate time series forecasting (MTSF), which leverages large language models (LLMs) to learn temporal dynamics among variables. The proposed approach addresses the limitations of existing statistical and deep learning-based methods, including limited learnable parameters and small-scale training data. By introducing a dual-modality encoding with two branches, TimeCMA extracts disentangled yet weak time series embeddings and entangled yet robust prompt embeddings from textual prompts. This cross-modality alignment retrieves both types of embeddings, allowing for more accurate forecasting. To reduce computational costs, the framework designs an effective prompt that encourages encapsulation of essential temporal information in the last token, which is then used to accelerate inference speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new approach called TimeCMA for predicting future time series data. It uses large language models to combine time series and text prompts, allowing it to learn patterns from both types of data. The method improves upon existing approaches by learning disentangled embeddings that represent different aspects of the data. This is achieved through a dual-modality encoding that separates time series and prompt information. By combining these two types of information, TimeCMA can make more accurate predictions about future events. The authors test their approach on eight real datasets and find that it outperforms existing methods. |
Keywords
» Artificial intelligence » Alignment » Deep learning » Inference » Prompt » Time series » Token
