Summary of Easytime: Time Series Forecasting Made Easy, by Xiangfei Qiu et al.
EasyTime: Time Series Forecasting Made Easy
by Xiangfei Qiu, Xiuwen Li, Ruiyang Pang, Zhicheng Pan, Xingjian Wu, Liu Yang, Jilin Hu, Yang Shu, Xuesong Lu, Chengcheng Yang, Chenjuan Guo, Aoying Zhou, Christian S. Jensen, Bin Yang
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 The EasyTime system simplifies the use of time-series forecasting methods by providing a one-click evaluation framework and an automated ensemble module. The framework leverages the preexisting time series forecasting benchmark (TFB) to facilitate evaluation using diverse datasets. The automated ensemble module combines promising forecasting methods to achieve superior accuracy compared to individual methods. Additionally, EasyTime offers a natural language Q&A module that converts user queries into SQL queries on the TFB database and returns answers in natural language and charts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EasyTime is a system that makes it easy for researchers and practitioners to use time-series forecasting methods. It has three main parts: one-click evaluation, automated ensemble, and natural language Q&A. The one-click evaluation lets you test new forecasting methods using different datasets. The automated ensemble combines the best methods together to get even better results. The Q&A module helps answer questions about which method is best for a specific type of data or question. |
Keywords
» Artificial intelligence » Time series
