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

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review


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.

Original languageEnglish
Title of host publicationArtificial Intelligence : Theory and Applications - Proceedings of AITA 2023
EditorsHarish Sharma, Antorweep Chakravorty, Shahid Hussain, Rajani Kumari
Number of pages11
Place of PublicationSingapore
PublisherSpringer Science and Business Media Deutschland GmbH
Publication date28.02.2024
ISBN (Print)978-981-99-8475-6
ISBN (Electronic)978-981-99-8476-3
Publication statusPublished - 28.02.2024
EventInternational Conference on Artificial Intelligence: Theory and Applications, AITA 2023 - Bangalore, India
Duration: 11.08.202312.08.2023

Bibliographical note

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

    Research areas

  • Hybrid prediction model, Machine learning, State-observation model
  • Engineering