A time series database is a collection of data that are generated in series as time goes on and constitutes a large portion of data stored in computers like stock-price movements, weather data, bio-medical measurements, video data etc., Two time sequences of same length are said to be similar if the Euclidean distance is less or equal to a given threshold. The main issue of similarity search in time series databases is to improve the search performance since time sequences are usually of high dimension. So it is important to reduce the search space for efficient processing of similarity search in large time series databases. We have used Adaptive Framework for the data reduction purpose which improves the search performance in Recent-Biased time series databases. We have applied a set of linear transformations on the reduced sequence that can be used as the basis for similarity queries on time series data. We have also formalized the intuitive notions of exact and approximate similarity in time series data. Our experiments show that the performance of this method is competitive to that of processing ordinary queries using the index and much faster than sequential scanning.

Key words: Similarity search, Data Reduction, Recent-Biased Time series, Adaptive Framework, Transformations.