A new model called Regime Switching Dynamic Correlation was developed to analyze relationships between different sets of data. This model breaks down the connections between data into two parts: correlations and standard deviations. The correlation between data sets can change depending on the regime, which is like a pattern or state. The transitions between these patterns are determined by a type of mathematical chain. This model is able to predict future changes in data more accurately than previous models, as shown in a real-world example.