point anomalies and `error_buffer`

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.

https://arxiv.org/abs/1802.04431

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point anomalies and `error_buffer` #59

Closed severous closed 3 years ago

severous commented 3 years ago

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())))

khundman commented 3 years ago

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.