Unfractionated heparin (UFH) is commonly used in the intensive care unit (ICU) to prevent blood clotting. Recently, many researchers focus on the development of data- driven methods to solve UFH related problems, which usually involves time series analysis. The performance of data-driven methods depends on whether the inter-correlation of attributes (or variables) in the dataset is closely examined and addressed. This study performs attribute selection, optimal time delay and inter-attributes relations on ICU time series data, in order to provide insights of time series data for UFH related problems. Medical records of 3211 patients with 22 attributes extracted from MIMIC (Medical Information Mart for Intensive Care) III database are used for the experiment. Experimental result shows that some of commonly selected attributes in the literature are less sensitive to the variations of UFH injection. Furthermore, some attributes are inter-dependent, which can increase the complexity of data-driven models, implying that the number of attributes could be reduced. There are 9 attributes found highly related and fast responding in 22 commonly used attributes. This study shows strong potential to provide clinicians with information about sensitive attributes that can help determine the UFH injection policy in ICU.