A Deep Learning Spatiotemporal Prediction Framework for Mobile Crowdsourced Services

TL;DR

This paper introduces a deep learning framework that predicts the spatial and temporal availability of mobile crowdsourced services by clustering services and forecasting their durations using advanced time series analysis.

Contribution

It presents a novel two-stage deep learning model combining clustering and time series forecasting for mobile crowdsourced service prediction.

Findings

Effective prediction accuracy demonstrated through experiments

Clustering improves spatial prediction precision

Time series forecasting accurately estimates service durations

Abstract

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.