Application of Big Data Technology in Dynamic Prediction Models based on Intelligent Algorithms

In the age of big data, large enterprises are challenged with processing vast amounts of data to make real-time decisions in a dynamically changing market. Traditional predictive models are obsolete, hence there is an urgent need for effective dynamic predictive models to improve decision-making efficiency and accuracy. This paper sets out to build a dynamic prediction model using intelligent algorithms applied on big data technologies such as TCN and data mining. First, multi-source data is aggregated through data integration technology. Second, principal component analysis is used to reduce the dimensionality of the feature space. Thirdly, cluster analysis is applied and used to identify user behavior patterns. Finally, XGBoost and TCN models are hybridized together. Finally, refining performance met with hyper-parameter optimization and model fusion. XGBoost is more than 96% accurate on average in prediction while processing real-time data with a time-latency of within 21 milliseconds. It basically speaks well to the improved ability of the dynamic prediction model based on intelligent algorithms in decision-making, providing new ideas and a practical basis for the application of big data technology.