Forecasting Demand of Shared Bikes Based on ARIMA Model

Yiming Wang

2024

Abstract

This research uses the ARIMA model to analyse and predict the hourly demand of shared bikes in Seoul, for the model is a better time series model and is suitable for studying the relationship between hours and bicycle demand. Firstly, the researcher processes the data and selects the data sets needed for the study from the original data. Secondly, the researcher conducts ADF testing on the data to detect whether differentiation is needed. Furthermore, through the ACF and PACF images, the p value and q value are determined. Finally, this paper plots against the fitted model and predicts five periods backward. The prediction results are consistent with the trend of the curve based on ARIMA model. The experiment yields hourly changes, which can help enterprises adjust bicycles’ number deployed in a timely manner. However, this research only studies the relationship between time and demand for shared bicycles, and do not consider the short-term impact of other factors, like weather and special events on demand. Subsequent researchers can conduct further research based on this paper.

Paper Citation

in Harvard Style

Wang Y. (2024). Forecasting Demand of Shared Bikes Based on ARIMA Model. In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-713-9, SciTePress, pages 659-664. DOI: 10.5220/0012961400004508

in Bibtex Style

@conference{emiti24,
author={Yiming Wang},
title={Forecasting Demand of Shared Bikes Based on ARIMA Model},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={659-664},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012961400004508},
isbn={978-989-758-713-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - Forecasting Demand of Shared Bikes Based on ARIMA Model
SN - 978-989-758-713-9
AU - Wang Y.
PY - 2024
SP - 659
EP - 664
DO - 10.5220/0012961400004508
PB - SciTePress