TY - CHAP
T1 - Using convolution to mine obscure periodic patterns in one pass
AU - Elfeky, Mohamed G.
AU - Aref, Walid G.
AU - Elmagarmid, Ahmed K.
PY - 2004
Y1 - 2004
N2 - The mining of periodic patterns in time series databases is an interesting data mining problem that can be envisioned as a tool for forecasting and predicting the future behavior of time series data. Existing periodic patterns mining algorithms either assume that the periodic rate (or simply the period) is user-specified, or try to detect potential values for the period in a separate phase. The former assumption is a considerable disadvantage, especially in time series databases where the period is not known a priori. The latter approach results in a multi-pass algorithm, which on the other hand is to be avoided in online environments (e.g., data streams). In this paper, we develop an algorithm that mines periodic patterns in time series databases with unknown or obscure periods such that discovering the period is part of the mining process. Based on convolution, our algorithm requires only one pass over a time series of length n, with O(nlogn) time complexity.
AB - The mining of periodic patterns in time series databases is an interesting data mining problem that can be envisioned as a tool for forecasting and predicting the future behavior of time series data. Existing periodic patterns mining algorithms either assume that the periodic rate (or simply the period) is user-specified, or try to detect potential values for the period in a separate phase. The former assumption is a considerable disadvantage, especially in time series databases where the period is not known a priori. The latter approach results in a multi-pass algorithm, which on the other hand is to be avoided in online environments (e.g., data streams). In this paper, we develop an algorithm that mines periodic patterns in time series databases with unknown or obscure periods such that discovering the period is part of the mining process. Based on convolution, our algorithm requires only one pass over a time series of length n, with O(nlogn) time complexity.
UR - http://www.scopus.com/inward/record.url?scp=35048882067&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-24741-8_35
DO - 10.1007/978-3-540-24741-8_35
M3 - Chapter
AN - SCOPUS:35048882067
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 606
EP - 620
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
A2 - Bertino, Elisa
A2 - Christodoulakis, Stavros
A2 - Koubarakis, Manolis
A2 - Plexousakis, Dimitris
A2 - Christophides, Vassilis
A2 - Bohm, Klemens
A2 - Ferrari, Elena
PB - Springer Verlag
ER -