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M. Suntinger, H. Obweger, J. Schiefer, P. Limbeck, G. Raidl:
"Trend-based similarity search in time-series data";
Vortrag: International Conference on Advances in Databases, Knowledge, and Data Applications (DBKDA), Menuires, FRANCE; 11.04.2010 - 16.04.2010; in: "Proceedings of the Second International Conference on Advances in Database, Knowledge, and Data Applications - DBKDA 2010", (2010), S. 97 - 106.

In this paper, we present a novel approach
towards time-series similarity search. Our technique relies on
trends in a curve´s movement over time. A trend is
characterized by a series´ values channeling in a certain
direction (up, down, sideways) over a given time period before
changing direction. We extract trend-turning points and utilize
them for computing the similarity of two series based on the
slopes between their turning points. For the turning point
extraction, well-known techniques from financial market
analysis are applied. The method supports queries of variable
lengths and is resistant to different scaling of query and
candidate sequence. It supports both subsequence searching
and full sequence matching. One particular focus of this work
is to enable simple modeling of query patterns as well as
efficient similarity score updates in case of appending new data
points.