## 5 Conclusions  
In addressing the need for high-performance networks to facilitate seamless roaming in FTIR, this paper considers, from the perspective of traffic prediction, improving resource allocation. Acknowledging the limitations of conventional traffic prediction methods, which often overlook backward information, we propose the CPO-BiTCN-BiLSTM-SA prediction model. This model leverages BiTCN-BiLSTM architecture to comprehensively extract traffic sequence features by causally inflating convolution and processing information through bidirectional time series analysis. Additionally, we introduced a SA mechanism to dynamically adjust the weights of each time step, thereby capturing key information within the sequence and enhancing prediction accuracy. Furthermore, the proposed model undergoes optimization via CPO to attain optimal hyperparameter settings that are tailored to its structure, thus enabling both single-step and multi-step flow prediction. The method was compared with other models five times, and the following conclusions were obtained:  
(1) In multi-step prediction scenarios, the accumulation of prediction errors correlates with an increasing number of prediction steps, consequently diminishing the prediction accuracy of the model.  
(2) The bidirectional structure inherent in the BiTCN-BiLSTM model can fully fetch the forward and backward information of the flow sequence, which improves the prediction accuracy.  
(3) Integration of the SA mechanism into the BiTCN-BiLSTM model facilitates the acquisition of weighted information, thus aiding in the capture of crucial model insights.  
(4) In comparison with the traditional XGBoost model, the proposed model has an average reduction of 29.50%, 25.43%, and 25.00% in the RMSE, MAE, and MAPE metrics, respectively, with a 6.70% improvement in the \(R^{2}\).  
The study presented herein demonstrates the efficacy of the CPO-BiTCN-BiLSTM-SA prediction model in significantly enhancing traffic prediction performance. Furthermore, the integration of multi-step traffic prediction offers a solution to the resource allocation challenges encountered in FTIR networks. In subsequent FTIR research, single-step prediction can be used to predict the newly arrived traffic data of ONU-AP during the waiting period of transmission so as to solve the problem that the waiting time of this part of data packets is too long (which results in an increase in the overall delay of the FTIR network). By using multi-step prediction to forecast the request of traffic data of multiple polling cycles in the future, the channel resources are occupied by control frame transmission and the delay increase caused by frequent dynamic bandwidth allocation calculation can be optimized; thus, the utilization of network bandwidth can be improved. Therefore, in order to reduce the impact of the traffic prediction model's performance on subsequent FTIR resource allocation in the future, it is necessary to continuously improve the accuracy of traffic model prediction, which is also the research significance of this paper.  
Conceptualization, B.C. and J.Z.; methodology, J.Z.; validation, J.Z. and Y.H.; formal analysis, B.C.; investigation, J.Z.; writing--original draft preparation, J.Z. and B.C.; writing--review and editing, J.Z. and Y.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (grant number 2021 YFB2900800) and the Science and Technology Commission of Shanghai Municipality (grant number 22511100902).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in this study.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.