Comparative Analysis of LSTM, ARIMA, and Facebook’s Prophet for Traffic Forecasting

Advancements, Challenges, and Limitations

Bachelor thesis (2023)

Authors

T.Z. Üzel Electrical Engineering, Mathematics and Computer Science

Contributors

E. Congeduti Computer Science & Engineering-Teaching Team - EEMCS (supervisor 1)
G. Iosifidis Networked Systems - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Ziyar Üzel
LSTM Time series analysis Time Series Forecasting Traffic forecasting ARIMA Prophet Facebook Meta Intelligent Transport Systems
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Published Date

28-06-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Accurate short-term traffic forecasting plays a crucial role in Intelligent Transportation Systems for effective traffic management and planning. In this study, the performances of three popular forecasting models are explored: Long Short-Term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), and Facebook's Prophet, for short-term traffic prediction. The models were trained and evaluated using a dataset of traffic flow data collected from 161 detectors over a specific time period. The experimental results reveal that ARIMA outperformed LSTM and Prophet in terms of Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). This suggests that while deep learning methods, such as LSTM, are generally acknowledged to outperform ARIMA in short-term traffic forecasting, this study reveals that there are specific scenarios where such well-accepted fact needs to be tested.