EasyTime: Time Series Forecasting Made Easy
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

TL;DR
EasyTime is a user-friendly system that simplifies time series forecasting by enabling one-click evaluation, automated ensemble methods, and natural language queries, thereby supporting researchers and practitioners.
Contribution
EasyTime introduces an integrated platform with evaluation, ensemble, and natural language modules to streamline time series forecasting tasks and facilitate method development.
Findings
Enables quick evaluation of forecasting methods across datasets.
Provides an ensemble approach that improves accuracy.
Includes a natural language interface for user-friendly interaction.
Abstract
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…
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Taxonomy
TopicsTime Series Analysis and Forecasting · Big Data and Business Intelligence
