A time sequence is a discrete sequence of values, e.g. temperature measure ments, varying over time. Conventional indexes for time sequences are built on the time domain and cannot deal with inverse queries on time sequences under some interpolation assumptions (i.e. computing the times when the values satisfy some conditions). To process an inverse query the entire time sequence has to be scanned.This thesis presents a dynamic indexing technique, termed the IP-index (Interpolation-index), on the value domain for large time sequences. This index can be implemented using regular ordered indexing techniques such as B-trees.Performance measurements show that this index dramatically improves the query processing time of inverse queries compared to linear scanning. For periodic time sequences that have a limited range and precision on their value domain (most time sequences have this property), the IP-index has an upper bound for insertion time and search time.The IP-index is useful in various applications such as scientific data analysis or medical symptom analysis. In this thesis we show how this index can be applied in the aeroplane navigation problem and dramatically improve the real-time performance.