[{"createTime":1735734952000,"id":1,"img":"hwy_ms_500_252.jpeg","link":"https://activity.huaweicloud.com/cps.html?fromacct=261f35b6-af54-4511-a2ca-910fa15905d1&utm_source=V1g3MDY4NTY=&utm_medium=cps&utm_campaign=201905","name":"华为云秒杀","status":9,"txt":"华为云38元秒杀","type":1,"updateTime":1735747411000,"userId":3},{"createTime":1736173885000,"id":2,"img":"txy_480_300.png","link":"https://cloud.tencent.com/act/cps/redirect?redirect=1077&cps_key=edb15096bfff75effaaa8c8bb66138bd&from=console","name":"腾讯云秒杀","status":9,"txt":"腾讯云限量秒杀","type":1,"updateTime":1736173885000,"userId":3},{"createTime":1736177492000,"id":3,"img":"aly_251_140.png","link":"https://www.aliyun.com/minisite/goods?userCode=pwp8kmv3","memo":"","name":"阿里云","status":9,"txt":"阿里云2折起","type":1,"updateTime":1736177492000,"userId":3},{"createTime":1735660800000,"id":4,"img":"vultr_560_300.png","link":"https://www.vultr.com/?ref=9603742-8H","name":"Vultr","status":9,"txt":"Vultr送$100","type":1,"updateTime":1735660800000,"userId":3},{"createTime":1735660800000,"id":5,"img":"jdy_663_320.jpg","link":"https://3.cn/2ay1-e5t","name":"京东云","status":9,"txt":"京东云特惠专区","type":1,"updateTime":1735660800000,"userId":3},{"createTime":1735660800000,"id":6,"img":"new_ads.png","link":"https://www.iodraw.com/ads","name":"发布广告","status":9,"txt":"发布广告","type":1,"updateTime":1735660800000,"userId":3},{"createTime":1735660800000,"id":7,"img":"yun_910_50.png","link":"https://activity.huaweicloud.com/discount_area_v5/index.html?fromacct=261f35b6-af54-4511-a2ca-910fa15905d1&utm_source=aXhpYW95YW5nOA===&utm_medium=cps&utm_campaign=201905","name":"底部","status":9,"txt":"高性能云服务器2折起","type":2,"updateTime":1735660800000,"userId":3}]
matlab One is generated by default in 2 * k + 1 Size window , The insufficient part is filled with the current data .
Estimation of standard deviation using normal distribution （ The value is 1.4826） * Absolute Median deviation , Get an estimated standard deviation .
matlab Zhongmo thinks 3 The outliers beyond the estimated standard deviation are determined .
The code is as follows ：
def hampel(X): length = X.shape[0] - 1 k = 3 nsigma = 3 iLo = np.array([i - k
for i in range(0, length + 1)]) iHi = np.array([i + k for i in range(0, length
+ 1)]) iLo[iLo < 0] = 0 iHi[iHi > length] = length xmad = [] xmedian = [] for i
in range(length + 1): w = X[iLo[i]:iHi[i] + 1] medj = np.median(w) mad =
np.median(np.abs(w - medj)) xmad.append(mad) xmedian.append(medj) xmad =
np.array(xmad) xmedian = np.array(xmedian) scale = 1.4826 # zoom xsigma = scale *
xmad xi = ~(np.abs(X - xmedian) <= nsigma * xsigma) # Find out the outliers （ That is, more than nsigma Standard deviation ） #
Replace outliers with median values xf = X.copy() xf[xi] = xmedian[xi] return xf X = np.array([1, 2, 3,
4, 100, 4, 3, 2, 1]) res = hampel(X) plt.plot(X) plt.plot(res, '--') plt.show()
The results are as follows ：
Outliers have been corrected , You can adjust the window size to your own appropriate value .