Taxi Demand Forecasting Utilizing Neighbourhood Influence Based on Ensemble Learning

Abstract

Abstract: This Time series forecasting (TSF) assists in making better strategic decisions under uncertain circumstances so that financial crisis can be avoided, wise investments can be made, under/over contracting of utility can be avoided, staffs can be scheduled appropriately, service providers can provide better service, mankind can get prepared for natural disasters and many more. However, the accuracy in forecasting plays a vital role and achieving such is a challenging task owing to the vagueness and nonlinearity associated with most of the real world time series. Therefore, improving the forecasting accuracy has become a keen area of interest among the forecasters from different domains of science and engineering. In this work, a time series forecasting model for Chicgo taxi trip data is proposed. The proposed model utilizes neighbourhood influence in terms of taxi demand to predict taxi demand. Keywords: Time series forecasting, Regularization, Regression