A prediction scheme using perceptually important points and dynamic time warping
作者：
Highlights：
• A novel, prediction scheme combining two celebrated data mining tools is proposed.
• PIPs are used to dynamically segment price series into subsequences.
• Dynamic time warping (DTW) is used to find similar historical subsequences.
• Predictions are made based from the mappings of the most similar subsequences.
• The proposed algorithm captures the deterministic structure in examined series.
摘要
•A novel, prediction scheme combining two celebrated data mining tools is proposed.•PIPs are used to dynamically segment price series into subsequences.•Dynamic time warping (DTW) is used to find similar historical subsequences.•Predictions are made based from the mappings of the most similar subsequences.•The proposed algorithm captures the deterministic structure in examined series.
论文关键词：Perceptually important points,Dynamic time warping,Nonlinear prediction,Efficient market hypothesis
论文评审过程：Available online 10 May 2014.
论文官网地址：https://doi.org/10.1016/j.eswa.2014.04.028
