Simulation of Effectiveness Evaluation Model in Complex Dataset Prediction Based on Neural Network Algorithm

With the advent of the big data era, various industries have generated massive datasets. Large scale time series datasets, as important and complex data objects, bring opportunities and challenges to various industries. By analyzing time series data, valuable information such as periodicity, basic trends, and association rules can be mined, and future trends can also be predicted. Time series prediction is a typical time series analysis task, which is of great significance for assisting decision-making, resource allocation, and taking stop loss measures in advance. Different application scenarios have different processing requirements for time series data, and these problems cannot be effectively solved using traditional time series methods. This article proposes an effectiveness evaluation model for time series prediction based on deep neural networks (DNN). Firstly, the original data is decomposed, and the decomposed components are input into the model as input vectors for fitting prediction. Finally, the fitting prediction results of each component are added together to obtain the final prediction result. The simulation results show that the model proposed in this article has higher accuracy and stability compared to traditional prediction models, and meets the prediction requirements of the model.