- 期刊
- OpenAccess
Dimensionality Reduction for Indexing Time Series Based on the Minimum Distance
並列摘要
We address the problem of efficient similarity search based on the minimum distance in large time series databases. To support minimum distance queries, most of previous work has to take the preprocessing step of vertical shifting. However, the vertical shifting has an additional overhead in building index. In this paper, we propose a novel dimensionality reduction technique for indexing time series based on the minimum distance. We call our approach the SSV-indexing (Segmented Sum of Variation Indexing). The proposed method can match time series of similar shape without vertical shifting and guarantees no false dismissals. Several experiments are performed on real data (stock price movement) to measure the performance of the SSV-indexing.
被引用紀錄
Luo, T. C. (2012). EDA和測試方法之於先進CMOS製程技術變異的特性分析與降低化 [doctoral dissertation, National Chiao Tung University]. Airiti Library. https://doi.org/10.6842/NCTU.2012.00154
魏伶容（2010）。鈦-8鋁-1釩-1鉬合金之退火結構〔碩士論文，國立屏東科技大學〕。華藝線上圖書館。https://doi.org/10.6346/NPUST.2010.00239
陳振民（2013）。多功能英語教學機器人之研發與應用〔碩士論文，國立虎尾科技大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0028-1007201315234200
延伸閱讀
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