## 1 Introduction  
The challenge in video prediction is to predict future video frames based on past observations. The most representative model is convolutional long short-term memory (ConvLSTM) [1], which captures both space and time simultaneously, thus extending the idea of a fully connected LSTM (FC-LSTM). ConvLSTM combines the traditional recurrent neural network (RNN) architecture with a convolutional structure. Compared with the FC-LSTM, ConvLSTM employs convolutional operations instead of fully connected operations, resulting in fewer network parameters and improved nonlinear modeling capabilities. Consequently, ConvLSTM has been widely used in image and video prediction, object detection, semantic segmentation, and other applications, showing good performance. Several studies have attempted to create variants of ConvLSTM [2, 3], including self-attention ConvLSTM (SA-ConvLSTM) [4]. The conventional ConvLSTM has limitations in capturing long-range spatial dependencies: therefore, SA-ConvLSTM was proposed to compensate for these limitations. SA-ConvLSTM retains the advantages of ConvLSTM and applies an attention mechanism and a self-attention module (SAM) to focus on data that are useful for prediction, suppressing those that are not. Other recent models include the random-mask video diffusion (RaMViD) model, which extends the image diffusion model to video using 3D convolution [5], and a nested attention module [6]. Despite surpassing traditional LSTM in handling spatial relationships, ConvLSTM struggles to capture long-term temporal dependencies, particularly in sequences in which important information is separated by long intervals. The self-attention mechanism is highly effective in capturing longer-range dependencies by allowing the inputs to interact with each other, thereby highlighting the inputs that are more closely focused on. This approach enables the model to develop a richer and more nuanced representation, offering a more comprehensive understanding of the entire sequence. In the field of video prediction, self-attention mechanisms facilitate the capture of dynamic motion. In this study, we utilize cells embedded with SAM called SA-ConvLSTM to apply the attention mechanism to ConvLSTM [7; 8; 9; 10]. SAM leverages the feature aggregation mechanism of self-attention to fuse both current and remembered features using pairwise similarity scoring. In an encoder-decoder structure, the encoder processes the input data and creates a contextual representation, whereas the decoder uses this context to generate the output. This process enables the models to effectively capture and utilize the context of the input data. By decomposing the complex task into two steps--encoding and decoding--the architecture adopts a more focused approach to understanding the input and generating the relevant output. In [4], a SA-ConvLSTM cell was proposed as a substitute for the ConvLSTM cell traditionally used in spatiotemporal predictions. They suggested that predicting the current timestep is feasible using relevant features from the past and introduced a self-attention memory module. This module is designed around a new memory unit that incorporates a self-attention mechanism. When combined with the conventional ConvLSTM, it forms SA-ConvLSTM. The innovation resides in the self-attention memory cell of SA-ConvLSTM, which leverages the newly proposed memory unit to utilize spatiotemporal information alongside global spatial and temporal data. SAM employs the self-attention mechanism to combine current and historical features. Consequently, the SA-ConvLSTM cell can capture global spatial dependencies more effectively than a traditional ConvLSTM cell with a single layer, thereby enhancing efficiency. Although conventional ConvLSTM can theoretically capture long-range spatial dependencies by stacking convolutional layers, this approach is not effective in practice. To address this issue, we developed an improved encoder-decoder model that incorporates SA-ConvLSTM instead of the existing ConvLSTM.