With the rapid development of mobile communication all over the world,the similarity of mobile phone communication data has received widely attention due to its advantage for the construction of smart cities.Mobile ph...With the rapid development of mobile communication all over the world,the similarity of mobile phone communication data has received widely attention due to its advantage for the construction of smart cities.Mobile phone communication data can be regarded as a type of time series and dynamic time warping(DTW)and derivative dynamic time warping(DDTW)are usually used to analyze the similarity of these data.However,many traditional methods only calculate the distance between time series while neglecting the shape characteristics of time series.In this paper,a novel hybrid method based on the combination of dynamic time warping and derivative dynamic time warping is proposed.The new method considers not only the distance between time series,but also the shape characteristics of time series.We demonstrated that our method can outperform DTW and DDTW through extensive experiments with respect to cophenetic correlation.展开更多
基金This work is supported in part by the National Natural Science Foundation of China and Civil Aviation Administration of China under grant No.U1533133the National Natural Science Foundation of China under grant No.61002016 and No.61711530653+2 种基金the Humanities and Social Sciences Research Project of Ministry of Education of China under grant No.15YJCZH095the China Scholarship Council under grant No.201708330439the 521 Talents Project of Zhejiang Sci-Tech University and the First Class Discipline B in Zhejiang Province:The Software Engineering Subject of Zhejiang Sci-Tech University.
文摘With the rapid development of mobile communication all over the world,the similarity of mobile phone communication data has received widely attention due to its advantage for the construction of smart cities.Mobile phone communication data can be regarded as a type of time series and dynamic time warping(DTW)and derivative dynamic time warping(DDTW)are usually used to analyze the similarity of these data.However,many traditional methods only calculate the distance between time series while neglecting the shape characteristics of time series.In this paper,a novel hybrid method based on the combination of dynamic time warping and derivative dynamic time warping is proposed.The new method considers not only the distance between time series,but also the shape characteristics of time series.We demonstrated that our method can outperform DTW and DDTW through extensive experiments with respect to cophenetic correlation.
