BROWSE

Related Researcher

Author's Photo

Lim, Sunghoon
Unstructured Data Mining and Machine Learning Lab
Research Interests
  • Unstructured Data Mining, Machine Learning, Industrial Artificial Intelligence (AI+X)

ITEM VIEW & DOWNLOAD

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

DC Field Value Language
dc.contributor.author Lim, Sunghoon ko
dc.contributor.author Kim, Sun Jun ko
dc.contributor.author Park, YoungJae ko
dc.contributor.author Kwon, Nahyun ko
dc.date.available 2021-07-15T07:39:57Z -
dc.date.created 2021-07-09 ko
dc.date.issued 2021-12 ko
dc.identifier.citation EXPERT SYSTEMS WITH APPLICATIONS, v.184, no.1, pp.115532 ko
dc.identifier.issn 0957-4174 ko
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/53190 -
dc.description.abstract 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. ko
dc.language 영어 ko
dc.publisher Pergamon Press Ltd. ko
dc.title A deep learning-based time series model with missing value handling techniques to predict various types of liquid cargo traffic ko
dc.type ARTICLE ko
dc.type.rims ART ko
dc.identifier.doi 10.1016/j.eswa.2021.115532 ko
dc.identifier.url https://www.sciencedirect.com/science/article/pii/S0957417421009404?via%3Dihub ko
Appears in Collections:
SME_Journal Papers

find_unist can give you direct access to the published full text of this article. (UNISTARs only)

Show simple item record

qrcode

  • mendeley

    citeulike

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

MENU