Forecasting e-waste recovery scale driven by seasonal data characteristics: A decomposition-ensemble approach.

Forecasting the scale of e-waste recycling is the basis for the government to formulate the development plan of circular economy and relevant subsidy policies and enterprises to evaluate resource recovery and optimise production capacity. In this article, the CH-X12 /STL-X framework for e-waste recycling scale prediction is proposed based on the idea of 'decomposition-integration', considering that the seasonal data characteristics of quarterly e-waste recycling scale data may lead to large forecasting errors and inconsistent forecasting results of a traditional single model. First, the seasonal data characteristics of the time series of e-waste recovery scale are identified based on Canova-Hansen (CH) test, and then the time series suitable for seasonal decomposition is extracted with X12 or seasonal-trend decomposition procedure based on loess (STL) model for seasonal components.

Analysis of the causes of moral injury in the outbreak of 2019-nCoV.

The infection of the novel coronavirus that originated from Wuhan, China in December 2019 converted rapidly into a pandemic by March 11, 2020. Whereas the infection mortality rate is not completely understood, it seems to be significantly beyond that of other recent pandemics (e.g.