Indoor Air Pollution Forecasting Using Deep Neural Networks

Jorge Altamirano-Astorga, Ita-Andehui Santiago-Castillejos, Luz Hernández-Martínez, Edgar Roman-Rangel

Abstract: Atmospheric pollution components have negative effects in the health and life of people. Outdoor pollution has been extensively studied, but a large portion of people stay indoors. Our research focuses on indoor pollution forecasting using deep learning techniques coupled with the large processing capabilities of the cloud computing. This paper also shares the implementation using an open source approach of the code for modeling time-series of different sources data. We believe that further research can leverage the outcomes of our research.