  This papers presents a deep learning-based framework to predict crowdsourced
service availability spatially and temporally. A novel two-stage prediction
model is introduced based on historical spatio-temporal traces of mobile
crowdsourced services. The prediction model first clusters mobile crowdsourced
services into regions. The availability prediction of a mobile crowdsourced
service at a certain location and time is then formulated as a classification
problem. To determine the availability duration of predicted mobile
crowdsourced services, we formulate a forecasting task of time series using the
Gramian Angular Field. We validated the effectiveness of the proposed framework
through multiple experiments.
