Anomaly detection k-means in Time Series I'm trying to use k-means to detect anomalies in the Amount column. I have the following part of my dataset:

8   2018-09-06  -2760.14
9   2018-10-04  -1149.73
10  2018-11-07  -1551.41
11  2018-12-06  -1208.17
12  2019-01-07   -244.02
13  2019-02-06    111.00
14  2019-03-06    139.24
15  2019-04-04 -27315.10
16  2019-05-07 -30326.58
17  2019-06-06  -1633.44


I used 2 as number of clusters and I get the following centroids:
[[  -237.045]
 [-28820.84 ]]
In my case the anomalies are rows 15 and 16 but now they form their own cluster so the distance to their centroid will be insignificant and thus not an anomaly
My question is, how can I detect the anomalies if they form their own cluster?
I don't suggest to use k-Means clustering for outlier detection. You can check the outlierness of observations by taking the standardized distance of each observation from the series' trend.

Alternatively, you can use DBSCAN algorithms: they are clustering models specifically designed to isolate outliers. I don't know if it makes sens on time series data. I'd go for the first method.