论文标题
部分可观测时空混沌系统的无模型预测
Approximate Order-Preserving Pattern Mining for Time Series
论文作者
论文摘要
保留订单的模式挖掘可以被视为发现时间序列的频繁趋势，因为相同的订单保留模式具有相同的相对顺序，可以代表趋势。但是，在存在数据噪声的情况下，许多有意义的模式的相对顺序通常相似而不是相同。为了挖掘时间序列中相似的相对订单，本文介绍了基于（Delta-Gamma）距离的近似订单的挖掘方法（AOP）挖掘方法，以有效地衡量相似性，并提出了一种称为AOP-Miner的算法，根据全球和局部近似参数来挖掘AOP AOP。 AOP-Miner采用模式融合策略来产生候选模式生成，并采用筛选策略来计算候选模式的支持。实验结果验证了AOP-Miner优于其他竞争方法，并且可以在时间序列中找到更多相似的趋势。
The order-preserving pattern mining can be regarded as discovering frequent trends in time series, since the same order-preserving pattern has the same relative order which can represent a trend. However, in the case where data noise is present, the relative orders of many meaningful patterns are usually similar rather than the same. To mine similar relative orders in time series, this paper addresses an approximate order-preserving pattern (AOP) mining method based on (delta-gamma) distance to effectively measure the similarity, and proposes an algorithm called AOP-Miner to mine AOPs according to global and local approximation parameters. AOP-Miner adopts a pattern fusion strategy to generate candidate patterns generation and employs the screening strategy to calculate the supports of candidate patterns. Experimental results validate that AOP-Miner outperforms other competitive methods and can find more similar trends in time series.
