Bike sharing systems are a new generation of traditional bike rentals where the whole process from membership, rental and return back has become automatic. Through these systems, user is able to easily rent a bike from a particular position and return back to another position. Currently, there are about over 500 bike-sharing programs around the world which are composed of over 500 thousand bicycles. Today, there exists great interest in these systems due to their important role in traffic, environmental and health issues.

Apart from interesting real-world applications of bike sharing systems, the characteristics of data being generated by these systems make them attractive for the research. Opposed to other transport services such as bus or subway, the duration of travel, departure and arrival position is explicitly recorded in these systems. This feature turns bike sharing system into a virtual sensor network that can be used for sensing mobility in the city. Hence, it is expected that most of important events in the city could be detected via monitoring these data.

This dataset contains the daily count of rental bikes between years 2015 and 2017 in Sina bikeshare system in Big D with the corresponding working day and season information. “Bike sharing.xlsx” dataset is available on Canvas with the following variables.

# Rides: count of rental bike

Day: Day 1 to day 731

Working day: 1 if it is a working day and 0 otherwise

Non-working day: 1 if it is a weekend and 0 otherwise

Winter: 1 if it is winter and 0 otherwise

Spring: 1 if it is spring and 0 otherwise 

Summer: 1 if it is summer and 0 otherwise

Autumn: 1 if it is autumn and 0 otherwise

Using the dataset answer the following questions:

1. Prepare a naïve forecast for day 2 through 731. Calculate the Mean Squared Error (MSE).

MSE:

2. Prepare a 2-day moving average forecast for day 3 through 731. Calculate MSE.

MSE:

3. Prepare an exponential forecast for day 1 through 731 with smoothing factor α=0.5. Calculate MSE. 

MSE:

4. Prepare a trend forecast for day 1 through 731. Consider the effect of different seasons (that is winter, spring, summer, and autumn) and working conditions (that is working days and non-working days)? Calculate MSE.

MSE:

5. Compare the forecasting approaches based on MSE. Which forecasting approach is superior?

Bike-Sharingxlsx