Time Series Data Prediction based on the Decision Tree Model

This paper explores the application of momentum mechanisms in predictive models, aiming to identify factors influencing momentum and develop a model for both quantitative and qualitative analyses. Using SPSS technology, technical statistical factors affecting the momentum of various events were first identified. These factors were used to build a decision tree model, which serves as a prediction tool to determine outcomes based on momentum characteristics. A flowchart was created through visualization analysis, illustrating that achieving “states of excellence in performance” aligns with the entity’s performance level. Binary logistic regression with a two-variable analysis (momentum and metrics) was recommended for assessing costs and diagnostic significance. ROC curve analysis revealed a strong positive correlation between the metrics and momentum, demonstrating a non-random pattern in their relationship. By working with time series data, the study effectively forecasts based on historical data. The findings provide a comprehensive understanding of how momentum can be quantitatively expressed and applied in predictive modeling for accurate and reliable forecasting across various fields.