The use of IoT technologies in various areas of our life, including environmental monitoring of green spaces, is increasing every year. One such solution is the TreeTalker sensor-based monitoring system, which collects data on various parameters of trees. One of the most important parameters is the rate of tree sap flow. Predicting the density of sap flow and studying the relationship between the parameters of trees and the environment is an urgent task. In this work, a statistical analysis of the data collected using the TreeTalker monitoring system was carried out. The data was pre-processed: outliers in the data were removed using mean value replacement, z-score replacement and cumulative moving average replacement. Groups of trees that were homogeneous in time were identified, and regression models were built to predict the sap flow parameter using auto-regressive moving average and linear modeling. The results obtained can be used for further studies of the dependence of the state of the tree on external factors.