A deep learning-based time series model with missing value handling techniques to predict various types of liquid cargo traffic

A deep learning-based time series model with missing value handling techniques to predict various types of liquid cargo traffic

Time series analysis

Long short-term memory

Vector autoregression

Autoregressive integrated moving average

Liquid cargo traffic prediction

Missing value handling technique

The authors propose a time series model that predicts future values of various types of liquid cargo traffic based on long short-term memory (LSTM), a deep learning technique. Existing liquid cargo traffic prediction models are based on traditional time series models, such as autoregressive integrated moving average (ARIMA) and vector autoregression (VAR). Some of these models, which do not consider linear dependencies among the values of different types of liquid cargo traffic, have limitations on their prediction performance, because the values of different types of liquid cargo traffic are dependent on one another. These models’ prediction performance are also limited due to the problem of vanishing gradients, which hinders the learning of long-range time series records. Missing values that exist on real-world liquid cargo traffic records reduce prediction performance as well. The proposed LSTM-based time series model handles missing values on liquid cargo traffic records and predicts future values of liquid cargo traffic. In addition, additional indices, such as inflation rates, exchange rates for dollars, GDP values, and the international prices of oil, are used to improve prediction performance. A case study involving real-world liquid cargo traffic records at the Port of Ulsan, Republic of Korea, for 216 months is used to validate prediction performance of the proposed LSTM-based prediction model compared with traditional ARIMA-based and VAR-based prediction models.

Expert Systems with Applications

2021

2021-12-01

Lim, Sunghoon

Kim, Sun Jun

Park, YoungJae

Kwon, Nahyun

journalArticle

0957-4174

10.1016/j.eswa.2021.115532

184

https://www.sciencedirect.com/science/article/pii/S0957417421009404