Ricerc@Sapienza

In recent years, hyper-complex deep networks (such as complex-valued and quaternion-valued neural networks - QVNNs) have received a renewed interest in the literature. They find applications in multiple fields, ranging from image reconstruction to 3D audio processing. Similar to their real-valued counterparts, quaternion neural networks require custom regularisation strategies to avoid overfitting. In addition, for many real-world applications and embedded implementations, there is the need of designing sufficiently compact networks, with few weights and neurons.

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.