Giters

khundman / telemanom

A framework for using LSTMs to detect anomalies in multivariate time series data. Includes spacecraft anomaly data and experiments from the Mars Science Laboratory and SMAP missions.

Home Page:https://arxiv.org/abs/1802.04431

Geek Repo:Geek Repo

Github PK Tool:Github PK Tool

point anomalies and `error_buffer`

severous opened this issue · comments

commented

Thanks for your brilliant work. I am confused about some questions.

  • It seems that all the anomalies in the smap dataset occur consecutively. I would like to know how you define point anomalies
  • I would like to know the purpose of setting the parameter error_buffer
i_anom = np.sort(np.concatenate((i_anom,
                                            np.array([i+buffer for i in i_anom])
                                             .flatten(),
                                            np.array([i-buffer for i in i_anom])
                                             .flatten())))
commented

This is explained in section 4.1 of the paper:

"Predicted anomalous regions are also slightly expanded to facilitate the combining of anomalous regions in close proximity – regions that overlap or touch after expansion are combined into a single region to account for situations where multiple anomalous regions represent a single event."

This also answers your second question, and take a look at #58 for more intuition of the error_buffer parameter.