Volume of Imbalance Container Prediction using Kalman Filter and Long Short-Term Memory

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet


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.

TitelArtificial Intelligence : Theory and Applications - Proceedings of AITA 2023
HerausgeberHarish Sharma, Antorweep Chakravorty, Shahid Hussain, Rajani Kumari
Anzahl der Seiten11
VerlagSpringer Science and Business Media Deutschland GmbH
ISBN (Print)978-981-99-8475-6
ISBN (elektronisch)978-981-99-8476-3
PublikationsstatusErschienen - 28.02.2024
VeranstaltungInternational Conference on Artificial Intelligence: Theory and Applications, AITA 2023 - Bangalore, Indien
Dauer: 11.08.202312.08.2023

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© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.