游戏收入预测数据模型

数据预处理规则：采用3σ准则剔除营收数据中的极端异常值（如突发大额充值、系统统计误差），对缺失的竞品营收数据采用“同品类+同期增长率”推算填充，保障数据完整性；时序预测规则：基于LSTM模型捕捉收入数据的周期性与趋势性特征，引入“节假日权重因子”适配游戏行业营收波动规律，短期预测准确率达93%以上；多因素归因算法：通过注意力机制强化“高价值用户付费”“核心促销活动”等关键因子的权重，解决传统模型因子贡献度模糊问题；模型迭代规则：每日增量更新行为与营收数据，每周进行模型重训，通过MAPE值（需≤8%）验证迭代效果，确保预测适配市场变化。

Data Preprocessing Rules: Adopt the 3σ criterion to remove extreme outliers from revenue data, including sudden large-scale game top-ups and system statistical errors. For missing competitor revenue data, perform imputation using the "same category + year-on-year growth rate of the corresponding period" method to ensure data integrity; Time Series Forecasting Rules: Utilize the LSTM model to capture the periodic and trend characteristics of revenue data, introduce a "holiday weight factor" to adapt to the revenue fluctuation patterns of the gaming industry, with the short-term prediction accuracy reaching over 93%; Multi-factor Attribution Algorithm: Strengthen the weights of key factors such as "high-value user payments" and "core promotional activities" through the attention mechanism, solving the problem of ambiguous factor contribution in traditional models; Model Iteration Rules: Conduct daily incremental updates of behavioral and revenue data, perform weekly model retraining, and validate the iteration effect via the MAPE value (required to be ≤8%) to ensure the forecasts adapt to market changes.