Quaternion widely linear forecasting of air quality
In this paper, we propose a quaternion widely linear approach for the forecasting of environmental data, in order to predict the air quality. Specifically, the proposed approach is based on a fusion of heterogeneous data via vector spaces. A quaternion data vector has been constructed by concatenating a set of four different measurements related to the air quality (such as CO, NO:2, SO:2, PM:10, an similar ones), then a Quaternion LMS (QLMS) algorithm is applied to predict next values from the previously ones.