Name
Abstract | ||
|---|---|---|
We target at the growing topic of representing and searching time-series data. A new MABI (Moving Average Based Indexing)
technique is proposed to improve the performance of the similarity searching in large time-series databases. Notions of Moving
average and Euclidean distances are introduced to represent the time-series data and to measure the distance between two series.
Based on the distance reducing rate relation theorem, the MABI technique has the ability to prune the unqualified sequences
out quickly in similarity searches and to restrict the search to a much smaller range, compare to the data in question. Finally
the paper reports some results of the experiment on a stock price data set, and shows the good performance of MABI method.
|
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-30466-1_27 | ER Workshops |
Keywords | Field | DocType |
indexation,similarity search,time series,euclidean distance,time series data,moving average | Information system,Data mining,Information integration,Similitude,Stock price,Indexation,Computer science,Search engine indexing,Euclidean geometry,Moving average | Conference |
ISSN | Citations | PageRank |
16113349 | 0 | 0.34 |
References | Authors | |
12 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| ziyu lin | 1 | 7 | 1.87 |
| 林子雨 | 2 | 129 | 10.80 |
| Yong-sheng Xue | 3 | 6 | 1.52 |
| 薛永生 | 4 | 0 | 0.34 |
| xiaohua lv | 5 | 21 | 1.88 |
| 吕晓华 | 6 | 0 | 0.34 |