Air Pollution Prediction Using an Ensemble of Dynamic Transfer Models for Multivariate Time Series

Entering a new era of big data, analysis of large amounts of real-time data is important, and air quality data as streaming time series are measured by several different sensors. To this end, numerous methods for time-series forecasting and deep-learning approaches based on neural networks have been used. However, they usually rely on a certain model with a stationary condition, and there are few studies of real-time prediction of dynamic massive multivariate data. Use of a variety of independent variables included in the data is important to improve forecasting performance. In this paper, we proposed a real-time prediction approach based on an ensemble method for multivariate time-series data. The suggested method can select multivariate time-series variables and incorporate real-time updatable autoregressive models in terms of performance. We verified the proposed model using simulated data and applied it to predict air quality measured by five sensors and failures based on real-time performance log data in server systems. We found that the proposed method for air pollution prediction showed effective and stable performance for both short- and long-term prediction tasks. In addition, traditional methods for abnormality detection have focused on present status of objects as either normal or abnormal based on provided data, we protectively predict expected statuses of objects with provided real-time data and implement effective system management in cloud environments through the proposed method.