Abstract
E-commerce has become a crucial business model through the Internet around the world. Therefore, its transaction trend forecast can provide important information for the market planning and development in advance. For this purpose, the integrated model of enhanced whale optimization algorithm (EWOA) with support vector machine (SVM) is proposed for forecast of E-commerce transaction trend in this study. First, the global optimization ability of the whale optimization algorithm (WOA) is enhanced by the search updating strategy. Second, multiple factors that may affect the E-commerce transaction trend are analyzed and determined using the gray correlation mechanism. Third, the EWOA algorithm is employed to optimize the SVM random parameters. Finally, the EWOA-SVM model is established for forecasting E-commerce transaction trend. Two representative cases tests confirm that the EWOA-SVM model is superior to other existing methods in terms of fast convergence speed and high prediction accuracy.
Highlights
For support vector machine (SVM) to be used for forecasting the E-commerce transaction trend, two main problems need to be resolved
Evaluation of Test Results. e SVM, whale optimization algorithm (WOA)-SVM, and enhanced Whale Optimization Algorithm (EWOA)-SVM models were used to predict the trend of E-commerce transactions in Cases 1 and 2. e prediction results of the model were evaluated
For Case 2, the maximum Re value for WOA-SVM and EWOA-SVM exceeded 27%. e prediction error of E-commerce in 2013 was relatively large, but the remaining errors were less than 20%. e overall prediction effects of WOA-SVM and EWOA-SVM models were better than that of SVM model
Summary
In SVM model, the penalty coefficient ρ and kernel coefficient δ are random parameters, which bring uncertainty to the prediction results under the complexity of the data To solve this problem, these random parameters need to be optimized. WOA, which has a strong optimization ability, is a new swarm intelligence optimization algorithm [36] It can simulate the predatory behavior of whales in nature, including foraging, encircling, bubble hunting, and food searching [37]. E prey is attacked through spiraling model when the food location is locked At this time, the location search updating strategy of whales is shown as follows [40, 41]: x(m + 1) x ∧(m) + B ∧ ebo cos(2πo), (9). It can be seen that the coefficient (da) value declines faster in the initial stage of the
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
