Summary of Multicast: Zero-shot Multivariate Time Series Forecasting Using Llms, by Georgios Chatzigeorgakidis et al.
MultiCast: Zero-Shot Multivariate Time Series Forecasting Using LLMs
by Georgios Chatzigeorgakidis, Konstantinos Lentzos, Dimitrios Skoutas
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the application of large language models (LLMs) for predicting future values in multivariate time series data. To overcome LLMs’ limitations in handling one-dimensional data, the authors introduce MultiCast, a zero-shot approach that enables LLMs to receive and process multivariate time series inputs. This is achieved through three novel token multiplexing solutions that reduce dimensionality while preserving key patterns. Additionally, the authors propose a quantization scheme to help LLMs learn these patterns more effectively, resulting in significant reductions in token use for practical applications. The performance of MultiCast is evaluated on three real-world datasets using RMSE and execution time metrics, demonstrating its effectiveness compared to state-of-the-art approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Predicting future values in multiple things that change over time is important in many areas. This paper looks at how large language models (LLMs) can be used for this task. LLMs are good at understanding text, but they usually work with one type of data at a time. To make them work with multiple types of data, the authors created something called MultiCast. It’s a special way to prepare the data so that LLMs can understand it and make predictions. The results show that MultiCast is better than other methods for this task. |
Keywords
» Artificial intelligence » Quantization » Time series » Token » Zero shot
