A Hybridly Optimized LSTM‐Based Data Flow Prediction Model for Dependable Online Ticketing

Abstract

Fifth-generation (5G) communication technologies and artificial intelligence enable the design and deployment of sophisticated solutions for enhanced user experience and superior network-based service delivery. However, the performance of the systems offering 5G-based services depends on various factors. In this paper, we consider the case of the online railway ticketing system in China that serves the needs of hundreds of millions of people daily. This system’s online access rates vary over time, and fluctuations are experienced, affecting its overall dependability and service quality. We use long short-term memory network, particle swarm optimization, and differential evolution to construct DP-LSTM—a hybridly optimized model to predict network flow for dependable and quality-enhanced service delivery. We evaluate the proposed model using real data collected over six months from the “12306 online ticketing” system. We compare the performance of the proposed model with mainstream network traffic prediction models. We use mean absolute percentage error, mean absolute error, and root mean square error for performance evaluation. Experimental results show the superiority of the proposed model.

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“…A profound learning-based model DPLSTM utilizing LSTM and crossover enhancement is described in [28] to resolve such issues in the "12306 tagging framework". This structure used a mean fundamental percentage error, mean absolute error, and a root means square error to assess the proposed model's exhibition utilizing genuine information.…”

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“…We define a data set X = fx 1 , x 2 , ⋯, x i , ⋯, x 12 g that represents the number of posts on the dark web from December 2019 to November 2020, where x i represents the number of posts on the dark web for the ith consecutive month since December 2019. The value of X is as follows: Theoretically, RNN can handle any long-distance dependency problem [22,23]. However, due to its gradient disappearance, gradient explosion, and other problems, RNN has only short-term memory and cannot achieve long-term preservation of information.…”

“…First, a linear model is applied on the time series to obtain the forecasts on linear component (L). Then, the residual series (e) is computed by dividing the forecasts on linear component (L) from the original time series y as in Equation (22). The residual series is used by a nonlinear model to get the forecasts on nonlinear component N. Then, the final forecasts are computed by multiplying the linear component forecasts with nonlinear component forecasts as in Equation (23).…”