Efficient and effective analytics for real-world time series forecasting

该项目旨在创建高效、有效的技术，为不同大小的不同时间序列集提供准确的预测。利用时间序列之间的相似性意味着在建立预测时使用许多相关的序列，而不是更大的序列。预期结果应该是创新的方法，以提高准确性，并允许用更短的时间序列进行预测。该项目解决了在短时间内准确利用大数据属性的需求，这正在改变许多行业。这将使零售、食品制造、运输、采矿、旅游、能源和科技等不同行业的预测更加准确和可靠。

This project aims to create efficient, effective techniques that provide accurate forecasts for heterogeneous sets of time series of varying sizes. Exploiting similarities between time series means using many related series, not larger series when building forecasts. The expected outcomes should be innovative methods that improve accuracy and allow forecasting with shorter time series. The project addresses the need to exploit properties of big data accurately in a short time frame, which is transforming many industries. This should enable more accurate and reliable forecasts across industries as diverse as retail, food manufacturing, transport, mining, tourism, energy, and technology.