# Multi-Dimensional Feature Fusion and Enhanced Attention Streaming Movie Prediction Algorithm
## 3 Fflstma
### Data Enhancement  
Streaming movie data are often relatively limited and have certain limitations, so data enhancement can effectively expand the data scale and enrich the diversity of data.  
Data flipping: Let the original box office data sequence be \(x=[x_{1},x_{2},\ldots,x_{n}]\), and the data flipping operation is to invert the sequence to obtain the flipped data sequence \(x_{flipped}=[x_{n},x_{n-1},\ldots,x_{1}]\). Mathematically, this is equivalent to performing a reversal operation on the sequence index; i.e., \(x_{flipped}(i)=x(n-i+1)\), where \(i=1,2,\ldots,n\). In this way, the model can learn the feature performance of the data in a different order; for the split box office data sequence, the forward and reverse sequences may contain different trend information; for example, the upward trend shows different patterns in the forward and reverse sequences, which helps the model to better understand the symmetric and asymmetric features of the data, and improves the ability of grasping the overall structure of the data. In the process of data enhancement, for each set of original data \(x\) and its corresponding label \(y\) (e.g., subsequent box office data), a set of flipped data \(x_{flipped}\) and flipped label \(y_{flipped}\) (also flipped for subsequent box office data) are generated and added to the training dataset, so as to expand the data scale and enrich the diversity of data.  
Data scaling: Let the original split box office data sequence be \(x=[x_{1},x_{2},\ldots,x_{n}]\), and the data scaling operation introduces a scaling factor \(s\) (randomly generated in the given range \([scale\_min,\ scale\_max]\)), where the scaled data sequence \(x_{scaled}=[s\cdot x_{1},s\cdot x_{2},\ldots,s\cdot x_{n}]\). Mathematically, it is represented as \(x_{scaled}(i)=s\cdot x(i)\), where \(i=1,2,\ldots,n\). This operation simulates the changes in movie split box office under different magnitudes, even though the fluctuation range of movie split box office in the original dataset is limited; after scaled enhancement, the model is able to reach out to a wider range of scenarios of changes in the split box office, and enhances the adaptability and sensitivity to the changes in the data magnitude. In data enhancement, for each set of original data \(x\) and its label \(y\), a set of scaled data \(x_{scaled}\) and corresponding scaled label \(y_{scaled}\) (i.e., \(y_{scaled}=s\cdot y\)) are generated and added to the training set, which can successfully increase the size and variety of the training data, lessen the model's undue reliance on a particular data distribution, and enhance the model's capacity for generalization, so as to make the model more adaptive and sensitive to the changes in data magnitude. In the face of complex and changing streaming movie market data, the model can more robustly and accurately predict the relevant indicators of the movie, and provide a more reliable basis and support for movie operation, promotion, and other decisions.  
In summary, the two data enhancement strategies, data flipping and data scaling, transform the original data from different perspectives, enrich the feature information and distribution of the data, and enhance the model's capacity for training and adaption to better handle the difficulties posed by data constraints and the intricacy of the streaming movie prediction assignment.