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.

中文翻译：

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基于增强型优化算法与支持向量机的电子商务交易趋势预测

电子商务已成为通过互联网在全球范围内的重要商业模式。因此，其交易趋势预测可以为市场提前规划和发展提供重要信息。为此，本研究提出了增强型鲸鱼优化算法（EWOA）与支持向量机（SVM）的集成模型，用于预测电子商务交易趋势。首先，通过搜索更新策略增强了鲸鱼优化算法（WOA）的全局优化能力。其次，利用灰色关联机制分析确定可能影响电子商务交易趋势的多个因素。第三，采用EWOA算法优化SVM随机参数。最后，建立EWOA-SVM模型对电子商务交易趋势进行预测。