# Multi-Dimensional Feature Fusion and Enhanced Attention Streaming Movie Prediction Algorithm
## 4 Results
### Introduction to Evaluation Indicators  
In order to evaluate the performance of the FFLSTMEA model proposed in this article in short-term prediction tasks of key indicators such as streaming movie revenue sharing and box office, the mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used to quantify the difference between predicted and true values. The suggested model's explanatory power on data variance was measured using the Coefficient of Determination (R-squared) to gauge the degree of fit between expected and actual values. The degree of variation between predicted and true values was also assessed. The MAE is primarily used to calculate the average absolute error between the actual and expected box office receipts for streaming movies. On average, it shows the degree of deviation between the expected and actual values in an intelligible manner, and its formula [28] is as follows:  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\lvert y_{i}-\hat{y}_{i}\rvert \tag{8}\]  
where \(y_{i}\) represents the true observed value of the ith sample, \(\hat{y}_{i}\) represents the model predicted value of the \(i\) sample, and \(n\) is the number of samples. The smaller the value of the MAE, the smaller the average deviation of the model predicted value from the true value, and the higher the predictive accuracy of the model. Figure 5: The collaboration network of directors and actors. The Formula (9) determines the average squared error between the true and anticipated values, which is what the MSE measures:  
\[\text{MSE}=\frac{1}{n}{\sum_{i=1}^{n}}(y_{i}-\hat{y}_{i})^{2} \tag{9}\]  
Larger mistakes are given greater weight in the computation since the MSE squares the errors. The MSE is more focused on penalizing larger errors than the MAE. In practice, if the model produces individually large prediction errors, the value of the MSE will increase significantly, thus reflecting the model's poor performance on these outliers. The RMSE, which is determined using the following formula, quantifies the sample standard deviation between the actual and anticipated streaming movie split box office values [28]:  
\[\text{RMSE}=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}}(y_{i}-\hat{y}_{i})^{2}} \tag{10}\]  
The RMSE is similar to the MSE, but it restores the magnitude of the error to the same level as the original data due to the open-square operation, making it easier to understand in a practical sense. For example, if the box office data are based on a specific box office counting unit, the value of the RMSE is the size of the average prediction error for each sample (according to the corresponding counting unit). The RMSE takes into account the errors of all samples and numerically visualizes the average deviation of the model's prediction from the true value, which is complementary to the MAE and provides a more comprehensive assessment of the model's accuracy. The formula for calculating MAPE, which measures the average percentage error of the projected value in relation to the real value, is as follows:  
\[\text{MAPE}=\frac{1}{n}{\sum_{i=1}^{n}}\frac{|y_{i}-\hat{y}_{i}|}{y_{i}}\times 1 00\% \tag{11}\]  
The benefit of the MAPE is that it can remove the impact of scale and provide the prediction error as a percentage, which is useful when comparing data from other models or scales. For the streaming movie box office in different movie types and time periods, the MAE and RMSE may be difficult to compare directly due to the difference in magnitude, while MAPE can provide a relatively uniform evaluation standard. The coefficient of determination (R-squared), which is determined using the following formula, is used to assess how well the model fits the data:  
\[\text{R}-\text{Squared}=1-\frac{{\sum_{i=1}^{n}}(y_{i}-\hat{y}_{i})^{2}}{{ \sum_{i=1}^{n}}(y_{i}-\overline{y})^{2}} \tag{12}\]  
where \(\overline{y}\) is the mean value of the real observations.