###### Abstract
By predicting network traffic accurately, operators can dynamically allocate resources, enhance resource utilization efficiency, and mitigate wastage. Currently, network traffic prediction methods are primarily studied in terms of three categories: traditional analysis, machine learning, and deep learning. The traditional analysis methods mainly include the classical Auto Regressive (AR) [2] and Moving Average (MA) [3] models, which have been applied to learning the linear characteristics of network traffic; in addition, these models have also been derived together into the Auto Regressive Moving Average (ARMA) [4] model. ARMA combines both AR and MA to obtain more accurate predictions than both. Subsequently, the Autoregressive Integrated Moving Average model (ARIMA) [5] was developed to build upon the foundations laid by ARMA. These traditional methods, owing to their simplicity, have found widespread application in network traffic prediction. However, they are limited to forecasting smooth time series, whereas real network traffic often exhibits non-smooth patterns. In response to the limitations of traditional methods, scholars have proposed machine learning methods, including K-Nearest Neighbor (KNN) [6], Extreme Gradient Boosting (XGBoost) [7], Support Vector Regression (SVR) [8], Extreme Learning Machine (ELM) [9], etc. While these methods offer improved capabilities for predicting non-stationary sequences, their performance diminishes on larger datasets and relies heavily on manual feature engineering, which is a time-intensive method. In contrast, deep learning approaches mitigate manual dependency and have gained traction in network traffic prediction. Recurrent Neural Network (RNN) [10], long short-term memory (LSTM) [11], Gated Recurrent Unit (GRU) [12], etc., are commonly employed deep learning models. These models exhibit enhanced capability in capturing intricate temporal dependencies within network traffic data, thus offering superior prediction performance compared to traditional and machine learning methods. Additionally, deep learning techniques demonstrate scalability and adaptability to diverse network environments, making them promising candidates for advancing the field of network traffic prediction. Additionally, existing network models still exhibit certain limitations. The hyperparameter settings of these models are often derived from prior research and may be somewhat subjective. Optimal hyperparameters can enhance network topology and improve generalization capabilities. Commonly utilized optimization algorithms for these models include Gradient-Based Optimizer (GBO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), etc. [PERSON] et al. [13] introduced the Crested Porcupine Optimizer (CPO) and conducted a comparative analysis against GBO, GWO, WOA, Equilibrium Optimization (EO), etc. This comparison utilized three standard CEC test functions and six real-world engineering problems. The results indicated that CPO outperforms other algorithms across various performance metrics, such as convergence speed and accuracy, in the majority of cases. However, existing traffic prediction models, such as the LSTM model proposed by [PERSON] et al. [14], as well as the TCN model adopted by [PERSON] et al. [15], mainly focus on the forward data characteristics of network traffic. These models ignore the implicit information embedded in reverse data, resulting in their inability to comprehensively capture the dynamic characteristics of network traffic. This, in turn, affects the prediction accuracy of traffic models, and it ultimately fails to meet the performance requirements of FTIR networks. Furthermore, existing research predominantly focuses on single-step prediction, which only anticipates traffic conditions for the next time step.