## 5 Conclusions  
In this study, we proposed a novel multi-layer encoder-decoder structure using SA-ConvLSTM for spatiotemporal prediction. SA-ConvLSTM innovatively integrates a self-attention memory module into the traditional ConvLSTM architecture, which significantly enhances its ability to capture long-range dependencies in both the spatial and temporal dimensions. This integration addresses a critical limitation of the standard ConvLSTM models, which often struggle with long-term dependencies manifested in complex spatiotemporal data. Our experimental results demonstrate that the models incorporating SA-ConvLSTM within an encoder-decoder framework surpass their standard ConvLSTM counterparts across various metrics. This performance improvement was particularly notable in scenarios involving intricate spatial relationships and extended temporal sequences. Furthermore, our findings revealed that models with a multi-layered encoder-decoder structure yielded markedly better predictions than models with a single layer, highlighting the importance of depth in capturing hierarchical spatiotemporal features. The ability to generate precise spatiotemporal predictions has far-reaching implications across various domains, facilitating smarter decision-making in fields such as weather forecasting, traffic prediction, video surveillance, and autonomous navigation. The broad spectrum of potential applications underscores the significance of our contribution to the field of spatiotemporal modeling. While SA-ConvLSTM demonstrates superior performance in video prediction relative to the conventional ConvLSTM structure, it is encumbered by prolonged training and inference times. This is attributed to the incorporation of the self-attention mechanism, which elevates the computational burden, particularly when handling high-resolution videos or lengthy sequences. In future work, we intend to conduct a comprehensive study on the impact of different hyperparameters of SA-ConvLSTM cells on model performance. This will enable us to further optimize the model, reduce training time, and extend its application to a variety of tasks, thereby improving prediction accuracy. This will include exploring different attention mechanisms, adjusting the number of layers in the encoder-decoder structure, and optimizing learning rates and batch sizes. Conceptualization, J.K.; methodology, J.K.; software, J.K.; validation, J.K.; formal analysis, J.K.; investigation, J.K.; resources, J.K.; data curation, J.K.; writing--original draft preparation, J.K.; writing--review and editing, J.K.; visualization, J.K.; supervision, J.J.; project administration, H.C. and J.J. All authors have read and agreed to the published version of the manuscript. This research was supported by Sung Kyun Kwan University and BK21 FOUR (Graduate School Innovation), funded by the Ministry of Education (MOE), South Korea and the National Research Foundation (NRF) of South Korea. And this work was partly supported by the Korea government (MSIT), IITP, Korea, under the ICT Creative Consilience program (RS-2020-II201821, 40%), AI Innovation Hub (RS-2021-II212068, 30%), and AI Graduate School Program (Sungkyunkwan University, RS-2019-II90421, 30%). Not applicable. All datasets are publicly available. The MovingMNIST dataset can be downloaded from [[https://www.cs.toronto.edu/~nitish/unsupervised_video/](https://www.cs.toronto.edu/~nitish/unsupervised_video/)]([https://www.cs.toronto.edu/~nitish/unsupervised_video/](https://www.cs.toronto.edu/~nitish/unsupervised_video/)) (accessed on 22 July 2024). The KTH dataset can be found at [[https://www.csc.kth.se/cvap/actions/](https://www.csc.kth.se/cvap/actions/)]([https://www.csc.kth.se/cvap/actions/](https://www.csc.kth.se/cvap/actions/)) (accessed 19 October 2024).