###### Abstract
Conversely, multi-step prediction not only offers a more comprehensive perspective for FTIR network resource allocation, thus enabling better decision making, but it also reduces the frequency of scheduling operations by forecasting traffic situations across multiple future polling cycles. This reduction in scheduling operations applies to both bandwidth allocation processes, involving REPORT request frames and GATE authorization frames, thus optimizing both uplink and upload signal utilization. Consequently, by predicting traffic patterns across multiple future polling cycles, the number of REPORT request frames and GATE authorization frames in bandwidth allocation processes is minimized, facilitating the efficient utilization of uplink channels and enhancing overall resource utilization. In addition, since FTTR has been proposed for a relatively short period of time, academic research related to it is still in the stages of exploration and development, and the research at this stage has seen proposals that traffic prediction can be applied to FTTR networks as a strategy. For example, [PERSON] [16] estimated the size of TCP service traffic through traffic prediction, determined whether the network will be congested, and then determined appropriate treatments in advance according to the judgment results. [PERSON] et al. [17] intelligently identified the types of services such as games, downloads, and videos in the network traffic by embedding the eAI module to ensure that the high-priority services could be transmitted with priority. [PERSON] et al. [18] conducted a study on the link layer, and they predicted the size of the physical layer protocols by predicting the size of the physical layer data unit message size. In addition, they reported the predicted triggered message size to the main gateway in advance to reduce the end-to-end transmission delay of FTTR messages. Although the above studies suggest that traffic prediction methods can be used in FTTR, they did not expand on what kind of prediction models they can be used for--so further research is needed. In this paper, building upon traditional single-step prediction approaches, we enhance the existing prediction model by introducing a novel traffic prediction model. Our proposed model integrates CPO with BiTCN and BiLSTM, and it is also complemented with a self-attention (SA) mechanism. BiTCN is adept at capturing the local features within time-series data, while BiLSTM excels at capturing long-term dependencies. The incorporation of the SA mechanism enables the model to selectively focus on the key features within the time series data that are relevant to the prediction target. Experimental results demonstrate the superior performance of the proposed BiTCN-BiLSTM-SA model across multiple datasets. The key innovations of this paper are as follows:  
(1) We propose a hybrid model, BiTCN-BiLSTM-SA, to tackle the issue of limited information extraction capacity in unidirectional networks. BiTCN is employed to capture the implicit relationships between traffic input and output data, which are then fed into BiLSTM for prediction. Additionally, the SA mechanism is integrated to amplify the impact of the crucial information on BiLSTM output, thereby enhancing the model's prediction accuracy. (2) Traditional TCN is used to perform forward convolutional computations on the input sequences for traffic prediction, thus overlooking the potential influence of backward information on prediction outcomes. This study addresses this limitation by introducing BiTCN, which integrates both forward and backward information, thus enhancing the extraction of the features from network traffic sequences. (3) The application of CPO to network traffic prediction solves the challenges of the BiTCN-BiLSTM-SA model with its high randomness and difficulty in selecting hyperparameters, and it also improves the accuracy of traffic prediction.