Application of Big Data Technology in Data Prediction Models Based on Deep Learning Algorithms

How to solve the problems faced by traditional statistical models such as large scale, variability and high complexity of data? In this regard, this article proposes a GAN algorithm. The article first defines the data prediction problem, defines the problem goal of this article as the prediction of stock prices, and then applies the adversarial generation network to the stock price prediction problem. Research has found that GAN can generate multiple possible stock price sequences, thereby providing multiple possibilities for future trends. Afterwards, the article establishes a prediction model based on big data, and completes the experimental test of the algorithm of this article through the model. The test results show that the data results of this model in the accuracy test are between 89% and 98%. By making full use of large-scale data samples and distributed computing capabilities, the model can better capture the complex associations and patterns in the data.