Forecasting air quality time series using deep learning

Deep Learning Architecture for Univariate Time Series Forecasting

This paper studies the problem of applying machine learning with deep architecture to time series forecasting. While these techniques have shown promise for modeling static data, applying them to sequential data is gaining increasing attention. This paper overviews the particular challenges present in applying Conditional Restricted Boltzmann Machines (CRBM) to univariate time-series forecastin...

Deep Belief Network Using Reinforcement Learning and Its Applications to Time Series Forecasting

Deep Learning for Real Time Crime Forecasting

Accurate real time crime prediction is a fundamental issue for public safety, but remains a challenging problem for the scientific community. Crime occurrences depend on many complex factors. Compared to many predictable events, crime is sparse. At different spatiotemporal scales, crime distributions display dramatically different patterns. These distributions are of very low regularity in both...

Indoor Air Quality Analysis Using Deep Learning with Sensor Data

Indoor air quality analysis is of interest to understand the abnormal atmospheric phenomena and external factors that affect air quality. By recording and analyzing quality measurements, we are able to observe patterns in the measurements and predict the air quality of near future. We designed a microchip made out of sensors that is capable of periodically recording measurements, and proposed a...

Machine Learning Strategies for Time Series Forecasting

The increasing availability of large amounts of historical data and the need of performing accurate forecasting of future behavior in several scientific and applied domains demands the definition of robust and efficient techniques able to infer from observations the stochastic dependency between past and future. The forecasting domain has been influenced, from the 1960s on, by linear statistica...