Deep Learning for Non-stationary Multivariate Time Series Forecasting

Forecasting time series that are generated in dynamic environments is challenging due to the characteristic of these series: data generated at high speed and/or large amounts of data which contain multiple variables, and have complex seasonality. Forecasting real-time availability of parking spaces can lead to all sorts of benefits including user satisfaction, energy savings and more efficient use of parking spaces. In this paper, we explorer using deep Gated Recurrent Units (GRUs) to forecast multivariate time series in parking lots setting. Predicting the parking availability mainly depends on the multiple seasonal patterns exhibited in the parking lots and by the real-time occupancy data. In particular, we use the heterogeneous data streams that generated from a limited number of existing Internet of Things (IoT) devices which monitor different parking lots, to construct the parking availability data. Then, we employ GRUs on the parking data to provide predictions across a variety of parking lots. Using the proposed approach, we have provided the real-time parking availability information for different time intervals in six parking lots at University of Essex. Although the results indicate the efficiency of applying GRUs to forecast the multivariate time series, these results show that the simple Multilayer Perceptron (MLPs) can perform better than the GRUs models.