Time-aware sport goods sale prediction for healthcare with privacy-preservation

Sports industry has been playing an important role in achieving good healthcare for public. However, with the advent of COVID-19, sports industry has been influenced significantly and the industry scale is decreased considerably. In this situation, how to accurately predict the sports industry scale in terms of production and consumption is becoming a practical and valuable task, because the whole world’s economy is not growing stably and users’ demand to sport goods is fluctuating sharply. However, three challenges are often existing in the sports industry scale prediction. First of all, there are so many kinds of sport goods that it is hard to quickly predict their future production or consumption scales accurately. Second, for a certain sport commodity, its production or consumption scale is often related to time especially in the COVID-19 environment. Third, sports industry scale data often contain some privacy, which probably disables data stakeholders to disclose their data. In view of these three challenges, a novel sports industry scale prediction approach (named SISP) is proposed for healthcare, which is basically according to time series analysis. Through SISP approach, we can quickly and accurately predict the future production or consumption scales of sport goods, in a privacy-aware way. At last, we validate the feasibility of the proposed SISP approach in this paper.