###### Abstract  
In order to solve the problem of traffic burst due to the increase in access points and user movement in an FTTR network, as well as to meet the demand for a high-performance network, it is necessary to rationally allocate network resources, and accurate traffic prediction is very important for dynamic bandwidth allocation in such a network. Therefore, this paper introduces a novel traffic prediction model, named CPO-BiTCN-BiLSTM-SA, which integrates the Crested Porcupine Optimizer (CPO), bidirectional temporal convolution (BiTCN), and bidirectional long short-term memory (BiLSTM) networks. BiTCN extends the traditional TCN by incorporating bidirectional data information, while BiLSTM enhances the network's capability to learn from long sequences. Moreover, self-attention (SA) mechanisms are utilized to emphasize the crucial segments in the data. Subsequently, the BiTCN-BiLSTM-SA model is optimized by CPO to obtain the best network hyperparameters, and model training prediction is performed to achieve multi-step predictions based on single-step prediction. To evaluate the model's generalization ability, two distinct datasets are employed for traffic prediction. Experimental findings demonstrate that the proposed model surpasses existing models in terms of the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (\(R^{2}\)). In comparison with the traditional XGBoost model, the proposed model has an average reduction of 29.50%, 25.43%, and 25.00% in RMSE, MAE, and MAPE, respectively, with a 6.70% improvement in \(R^{2}\). F +
Footnote †: journal: Applied sciences  
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Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))). **Citation:** [PERSON]; [PERSON]; [PERSON] Research on the Fiber-to-the-Room Network Traffic Prediction Method Based on Crested Porcupine  
**Opentime:** Optimization. _Appl. Sci._** 2024, 14, 4840. [[https://doi.org/10.3390/app14114840](https://doi.org/10.3390/app14114840)]([https://doi.org/10.3390/app14114840](https://doi.org/10.3390/app14114840))  
**2024, 14, 4840. [[https://doi.org/10.3390/app14114840](https://doi.org/10.3390/app14114840)]([https://doi.org/10.3390/app14114840](https://doi.org/10.3390/app14114840))**  
**1. Introduction**  
With the rapid evolution of the Internet--coupled with the widespread adoption of online education, e-commerce, high-definition video streaming, and similar applications--there has been a significant surge in people's demand for network bandwidth and enhanced quality of service. This surge has, in turn, accelerated the expansion of fiber-optic bandwidth networks. However, through a large amount of research, it has been found that optical fiber has found its way into the home, but it is currently only being directed to a home user's information box, resulting in operators trying to provide high-bandwidth services but not being able to give full play to the optical fiber to operate in the \"Gigabit into the home is easy, a hundred megabits into the house is difficult to the\" status quo. In response to these challenges, the telecom industry has proposed the concept of \"fiber-to-the-room (FTTR)\" connectivity [1], which is aimed at ensuring high-speed network access within residential premises through the convergence of FTTR and Wi-Fi to achieve seamless network roaming. To facilitate seamless roaming, it is imperative to allocate network resources judiciously, ensuring minimal conflict or loss, thereby maximizing bandwidth utilization and delivering stable and reliable network connectivity services to users. Accurate analyses and predictions of network traffic can serve as effective mechanisms for optimizing resource utilization.