# Multi-Dimensional Feature Fusion and Enhanced Attention Streaming Movie Prediction Algorithm
## 2 Related Work  
The history of research in time prediction modeling is one of continuous evolution and gradual transition from traditional statistical methods to modern deep learning techniques. The increasing need for complicated time series data processing in data science is reflected in this evolution process. Initially, statistical techniques like autoregressive (AR) models, moving average (MA) models, and autoregressive moving averages (ARMAs) were the primary means of time series prediction. However, the simple statistical model has some limitations in the process of time prediction [8], so in order to overcome these limitations, ARIMA [9] was introduced into time series forecasting. And with the advent of the big data era, the rapid development of deep learning has brought a brand new breakthrough in temporal prediction modeling. The recurrent neural network (RNN) model and its variant, LSTM, successfully capture the complex long-term dependencies in sequential data by virtue of their built-in recurrent structure and gating units. Based on the LSTM network model, [PERSON] et al. [10] modified the starting weights and thresholds to increase the economic forecasting effect of the LSTM model, which was used to predict economic indicators. [PERSON] et al. [11] corrected the commonly employed LSTM in time series under the consideration of multivariate scenarios and selected the stock prices for forecasting. [PERSON] et al. [12] employed STL decomposition to characterize the iron ore futures price, and then designed a convolutional neural network (CNN)-LSTM model combined with self-attention in order to predict iron ore futures prices, before finally performing comparative experiments. [PERSON] et al. [13] utilized the Massively Parallel Long Short-Term Memory (MHPLSTM) model in an emotion recognition task and merged the outputs of the MHPLSTM model through a splicing layer to further improve the translation quality. In the processing of small-sample data, [PERSON] et al. [14] improved the network topology, used Euclidean Paradigm (Second Norm, L2) regularization to enhance small-sample models' adaptation capacity, and improved the robustness of the model to noise by reducing the model's sensitivity to noise. With the advancement of technology, neural networks and other advanced technologies have been introduced into movie-related research. [PERSON] et al. [15] created a neural network-related model with a multi-feature attention mechanism by mining and integrating various features related to movies. Then, with the help of attention systems,different weights can be assigned to different features to improve the ability to capture user personalized preferences and generate more accurate movie customization recommendation services that are consistent with user interests. In a multimodal sentiment analysis, [PERSON] et al. (2016) aimed to extract features from different modal data, using the attention mechanism to achieve data enhancement and the reorganization and fusion of the extracted features, so that the fused features could better reflect the emotional information in the data, thus improving the accuracy and effectiveness of multimodal sentiment analysis. By focusing on the visual modality of movie posters, [PERSON] et al. (2017) focused on feature fusion through the multimodal Transformer, added the attention mechanism to the poster features in the fusion process, efficiently integrated more modal information with the poster's visual content to give the movie recommendation algorithm deeper and more useful feature inputs, and ultimately realized a more reasonable and accurate movie recommendation. [PERSON] et al. (2018) used the neural network model's potent nonlinear fitting capability to model and analyze the various elements influencing a movie's box office performance. He also mined audience sentiments and other movie-related data and combined them to create a box office prediction model that should more accurately forecast a movie's box office performance. [PERSON] et al.