The TS Data Preparation Node includes which of the following functionalities?

The TS Data Preparation Node is specifically designed to handle the nuances of time series data, which includes functionalities tailored for cleaning and transforming that data to make it suitable for analysis. This node helps in addressing issues such as handling missing values, adjusting for seasonality, and ensuring that the data is in the right format for subsequent modeling tasks.

Time series data often requires special attention because its structure differs from typical datasets. For instance, it might involve trends or seasonal patterns that need to be accounted for during preparation. The TS Data Preparation Node automates these transformations and cleaning processes, making it more effective for time series analysis as compared to a general data preparation approach.

In contrast, modeling survival data, reducing dimensions of data, and generating correlation coefficients pertain to different aspects of data analysis and preparation, which are not the primary focus of the TS Data Preparation Node. These tasks might be handled by other nodes that specialize in statistical modeling, dimensionality reduction, or correlation analysis respectively.