EasyTime：让时间序列预测变得简单
EasyTime: Time Series Forecasting Made Easy
AI总结：
EasyTime系统通过一键评估、自动集成模块和基于大语言模型的自然语言问答模块，简化了时间序列预测方法的使用，并支持新预测方法的开发。
摘要：
时间序列预测在各个领域都有重要应用。我们演示的系统EasyTime使得研究人员和从业者能够轻松使用时间序列预测方法。首先，EasyTime支持一键评估，使研究人员能够利用预先存在的时间序列预测基准（TFB）中收集的多样化时间序列数据集来评估新的预测方法。这是通过利用TFB灵活且一致的评估流程来实现的。其次，当从业者必须在新数据集上进行预测时，一个重要的第一步通常是找到合适的预测方法。EasyTime提供了一个自动集成模块，该方法结合了有前景的预测方法，与单个方法相比，能产生更优的预测精度。第三，EasyTime提供了一个利用大语言模型（LLM）的自然语言问答模块。对于诸如“哪种方法最适合对具有强季节性的时间序列进行长期预测？”的问题，EasyTime将其转换为对TFB获得的结果数据库的SQL查询，然后以自然语言和图表形式返回答案。通过演示EasyTime，我们旨在展示如何简化时间序列预测的使用，并为新一代时间序列预测方法的开发提供更好的支持。
英文摘要：
Time series forecasting has important applications across diverse domains. EasyTime, the system we demonstrate, facilitates easy use of time-series forecasting methods by researchers and practitioners alike. First, EasyTime enables one-click evaluation, enabling researchers to evaluate new forecasting methods using the suite of diverse time series datasets collected in the preexisting time series forecasting benchmark (TFB). This is achieved by leveraging TFB's flexible and consistent evaluation pipeline. Second, when practitioners must perform forecasting on a new dataset, a nontrivial first step is often to find an appropriate forecasting method. EasyTime provides an Automated Ensemble module that combines the promising forecasting methods to yield superior forecasting accuracy compared to individual methods. Third, EasyTime offers a natural language Q&A module leveraging large language models. Given a question like "Which method is best for long term forecasting on time series with strong seasonality?", EasyTime converts the question into SQL queries on the database of results obtained by TFB and then returns an answer in natural language and charts. By demonstrating EasyTime, we intend to show how it is possible to simplify the use of time series forecasting and to offer better support for the development of new generations of time series forecasting methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
