Volume of Imbalance Container Prediction using Kalman Filter and Long Short-Term Memory
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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Artificial Intelligence: Theory and Applications - Proceedings of AITA 2023. Hrsg. / Harish Sharma; Antorweep Chakravorty; Shahid Hussain; Rajani Kumari. Singapore: Springer Science and Business Media Deutschland GmbH, 2024. S. 53-63 (Lecture Notes in Networks and Systems; Band 843).
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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TY - CHAP
T1 - Volume of Imbalance Container Prediction using Kalman Filter and Long Short-Term Memory
AU - Kim, Geunjeong
AU - Block, Brit Maren
AU - Mercorelli, Paolo
N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024/2/28
Y1 - 2024/2/28
N2 - This paper develops a hybrid prediction model KALSTM that is a combination of a Kalman filter and a well-known neural network-based prediction model, Long Short-Term Memory (LSTM), to generate prediction results from the data, more specifically the weekly number of imbalance 20 general (GE) dry cargo container at Hamburg depots in Germany. The given data has been split into training and testing set. The training set has been used as input for the LSTM model to forecast the future weekly number of imbalance 20 GE containers. Even if the result from the LSTM model normally outperforms other existing forecasting models, it is still an approximation to the future because there are factors such as international political situations, overseas economic trends, and any kind of unexpected cases which increase the uncertainty of the prediction. Therefore, the output of LSTM has been used as container model xk, x^k of Kalman filter which filters the noise out with container measurement zk applying other forecasting models. Thus, a Kalman filter has been applied to improve the output of LSTM by correcting the noise of it. The prediction results of KALSTM demonstrate improved predictive performance based on common metrics such as mean squared error (MSE), root mean squared error (RMSE), and R2. Thus, the results can help decision-makers to improve tracking the volume of imbalance containers and to better prepare for further investigation to deal with the uncertainty of imbalance container volume.
AB - This paper develops a hybrid prediction model KALSTM that is a combination of a Kalman filter and a well-known neural network-based prediction model, Long Short-Term Memory (LSTM), to generate prediction results from the data, more specifically the weekly number of imbalance 20 general (GE) dry cargo container at Hamburg depots in Germany. The given data has been split into training and testing set. The training set has been used as input for the LSTM model to forecast the future weekly number of imbalance 20 GE containers. Even if the result from the LSTM model normally outperforms other existing forecasting models, it is still an approximation to the future because there are factors such as international political situations, overseas economic trends, and any kind of unexpected cases which increase the uncertainty of the prediction. Therefore, the output of LSTM has been used as container model xk, x^k of Kalman filter which filters the noise out with container measurement zk applying other forecasting models. Thus, a Kalman filter has been applied to improve the output of LSTM by correcting the noise of it. The prediction results of KALSTM demonstrate improved predictive performance based on common metrics such as mean squared error (MSE), root mean squared error (RMSE), and R2. Thus, the results can help decision-makers to improve tracking the volume of imbalance containers and to better prepare for further investigation to deal with the uncertainty of imbalance container volume.
KW - Hybrid prediction model
KW - Machine learning
KW - State-observation model
KW - Engineering
UR - http://www.scopus.com/inward/record.url?scp=85187643010&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-8476-3_5
DO - 10.1007/978-981-99-8476-3_5
M3 - Article in conference proceedings
AN - SCOPUS:85187643010
SN - 978-981-99-8475-6
T3 - Lecture Notes in Networks and Systems
SP - 53
EP - 63
BT - Artificial Intelligence
A2 - Sharma, Harish
A2 - Chakravorty, Antorweep
A2 - Hussain, Shahid
A2 - Kumari, Rajani
PB - Springer Science and Business Media Deutschland GmbH
CY - Singapore
T2 - International Conference on Artificial Intelligence: Theory and Applications, AITA 2023
Y2 - 11 August 2023 through 12 August 2023
ER -