Summary of An Evaluation Of Standard Statistical Models and Llms on Time Series Forecasting, by Rui Cao and Qiao Wang
An Evaluation of Standard Statistical Models and LLMs on Time Series Forecasting
by Rui Cao, Qiao Wang
First submitted to arxiv on: 9 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 research explores the limitations of Large Language Models (LLMs) in predicting time series, specifically examining the LLMTIME model. Despite their success in tasks like text generation, language translation, and sentiment analysis, LLMs struggle with time series prediction due to challenges such as diverse datasets and traditional signals. The study assesses LLMTIME’s performance across multiple datasets and introduces classical almost periodic functions to evaluate its effectiveness. Results show that while LLMTIME performs well in zero-shot forecasting for certain datasets, its predictive accuracy declines significantly when faced with complex time series data featuring both periodic and trend components, as well as complex frequency components. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super smart computers that can do lots of cool things like generate text or translate languages. But what happens when we try to use them for something new? Like predicting what will happen in the future based on past data? This study looked at how well these LLMs, specifically one called LLMTIME, could predict what would happen next in a series of numbers. They found that while LLMTIME was good at some things, it struggled with others, especially when the data was complex and had many different patterns. This is important because we want to use machines like LLMTIME to help us make predictions about all sorts of things. |
Keywords
» Artificial intelligence » Text generation » Time series » Translation » Zero shot
