关于电影产业发展趋势的基于分解整合的预测研究
1School of Economics and Management, Beijing Information Science and Technology University, 12 Xiaoying East Road, Haidian District, Beijing, PR China.
Heliyon
|November 13, 2023
概括
这项研究介绍了一种新的电影票房预测模型,使用集体实证模态分解 (EEMD) 和粒子群优化 (PSO) 与最小平方支持向量机器 (LSSVM). EEMD-PSO-LSSVM模型有效地预测了波动的,非线性票房收入.
科学领域:
- 经济学 经济学 经济学
- 数据科学数据科学数据科学
- 计算金融是指计算金融.
背景情况:
- 预测电影行业的增长至关重要,因为电影在创造收入和传播文化方面发挥着双重作用.
- 票房收入数据带来了诸如波动性和小样本规模等挑战,阻碍了准确的预测.
- 现有的模型与时间序列票房数据的非线性,非光滑的特征作斗争.
研究的目的:
- 开发一个先进的电影票房预测模型,解决数据波动和小样本问题.
- 整合分解和预测技术,以提高预测准确度.
- 建立一个可靠的模型来预测电影行业的财务发展轨迹.
主要方法:
- 综合实证模态分解 (EEMD) 将历史票房数据分解为内在模式函数.
- 粒子集群优化 (PSO) 用于优化最小平方支持向量机 (LSSVM) 的参数.
- 结合来自单个分解序列的预测,使用整合方法形成最终预测.
主要成果:
- EEMD-PSO-LSSVM模型有效地捕捉了季度电影票房收入的波动特征.
- 分解-整合策略在处理复杂数据模式方面取得了成功.
- 实验结果验证了与其他基准模型相比,该模型的卓越性能.
结论:
- 拟议的EEMD-PSO-LSSVM模型在预测非线性,非平滑和小样本时间序列票房数据方面取得了重大进展.
- 分解整合方法对于分析和预测波动的金融时间序列非常有效.
- 该模型为理解和预测电影行业票房表现的未来方向提供了可靠的工具.
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