Indoor localisation is used for locating the objects and people within the buildings where our outdoor tracking tools and technologies cannot provide precise results due to the unavailability of any visual contact with the satellites used by them. With the advancement of Internet of Things (IoT) devices, indoor localisation in indoor buildings like airports, hotels, and tourist places is possible. This paper aims to improve the analytics research, focusing on data collected through indoor localisation methods. Our smart devices constantly try to improve the user experience by recurrently broadcasting automatic connectivity requests. These packets are known as Wi-Fi probe requests and can encapsulate various spatio-temporal information of the device carrier. The objective of this paper is to perform a detailed comparison between the Prophet model developed by Facebook and our implementation of the Auto Regressive Moving Average (ARMA) model. Prophet Model is an additive model that requires no manual effort and can easily detect and handle outliers or missing data.In contrast, the ARMA model may require more effort and deep statistical analysis but allows the user to tune it and reach a more personalised result. Second, we have attempted to understand human behaviour. We have used historical data to forecast using two different models, which we conclude by comparing. Subsequently, we mapped each probe request to the section of our place of interest where it was captured while also considering its uncertainty radius. Finally, we have performed pedestrian flow analysis by identifying the most common paths followed inside our place of interest.