Summary of Ltsm-bundle: a Toolbox and Benchmark on Large Language Models For Time Series Forecasting, by Yu-neng Chuang et al.
LTSM-Bundle: A Toolbox and Benchmark on Large Language Models for Time Series Forecasting
by Yu-Neng Chuang, Songchen Li, Jiayi Yuan, Guanchu Wang, Kwei-Herng Lai, Songyuan Sui, Leisheng Yu, Sirui Ding, Chia-Yuan Chang, Qiaoyu Tan, Daochen Zha, Xia Hu
First submitted to arxiv on: 20 Jun 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 In the field of Time Series Forecasting (TSF), researchers have been inspired by Large Language Models (LLMs) to develop Large Time Series Models (LTSMs). These transformer-based models use autoregressive prediction to improve TSF. However, training LTSMs on heterogeneous time series data poses unique challenges, including diverse frequencies, dimensions, and patterns across datasets. This paper introduces LTSM-Bundle, a comprehensive toolbox and benchmark for training LTSMs, encompassing pre-processing techniques, model configurations, and dataset configuration. By modularizing and benchmarking LTSMs from multiple dimensions, including prompting strategies, tokenization approaches, training paradigms, base model selection, data quantity, and dataset diversity, this study identifies the most effective design choices. The results demonstrate that the combination of these design choices achieves superior zero-shot and few-shot performances compared to state-of-the-art LTSMs and traditional TSF methods on benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to improve time series forecasting, which is predicting future events based on past patterns. The researchers were inspired by how well big language models do at understanding human languages and decided to apply that idea to time series data. But they faced some challenges because the different datasets had different characteristics, such as frequency and size. To overcome these challenges, the researchers created a toolkit called LTSM-Bundle, which includes many different settings and configurations for training large time series models. By testing all of these options, they found that combining certain strategies worked better than others. This new approach outperformed other state-of-the-art methods in predicting future events. |
Keywords
» Artificial intelligence » Autoregressive » Few shot » Prompting » Time series » Tokenization » Transformer » Zero shot
