y=2.907+0.007x, as shown in Fig. 1. Based on the Definition 2, we present our RDD algorithm.

![0_image_0.png](0_image_0.png)

Given a time series data S = d1d2……dN, denote point (i,di) by pi, and pjpipk by jik
(degree). Relative Deviant Degree: **Algorithm 1** Input: time series data S Output: RDDs of S
- Specify a similarity function sim(pk,(pi,pj))=exp(-*jik^2/50)*, denoted byij k s . 

- Specify an offset function off(pk,(pi,pj)): distance of k to line Lij, denoted byij k o .

for ( i from 1 to N ) {

![0_image_1.png](0_image_1.png)

 }

$$\operatorname{sr}(\,i,j\,{\mathrm{from}}\,1)$$

 for( i, j from 1 to N, j *≠ i* )

![0_image_2.png](0_image_2.png)

![0_image_3.png](0_image_3.png)

 