Paper Title
Metro Passenger Volume Forecast based on Time Series Models

Abstract
This paper represents the adaptability to forecast a short-term metro passenger data for a month based on linear regression model. Location considered for this study is of two of busiest corridors of Hyderabad metro since its inception. This report includes comprehensive information on historical passenger traffic in a particular segment. Time series models for forecasting that are being used in this paper are ARIMA, prophet, and TBATS.With the opening of a new metro line and the expansion of an existing one, in comparison to the ARIMA model, the MELR model is more effective in making short-term predictions about the sudden volume. Keywords - MRT, ARIMA, TBATS, Forecast, Time Series, Uni-Variant Series, Linear Regression