Besides being a fundamental infrastructure for communication, cellular networks are exploited for positioning through signal fingerprinting. Maintaining the fingerprint database consistent and up-to-date is a challenging task in many fingerprint positioning systems, e.g., in those populated by a crowd-sourcing effort. To this end, detecting and tracking the changes in the configurations of cellular networks over time is recognized as a relevant problem. In this paper, we show that to cope with this problem we can successfully exploit information provided by Timing Advance (TA). As a by-product, we prove that TA can improve the fingerprint candidate selection phase, reducing the number of fingerprints to provide as input to positioning algorithms. The effectiveness of the proposed improvements has been tested on a fingerprint positioning system with a large fingerprint dataset collected over a period of 2 years.