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Questions - anomaly-detection

Why does my autoencoder generate the Anomalous image instead of the anomaly-free image?

I really need help with this, I am working on an anomaly detector project using Autoencoder, the first part of the project is to build an autoencoder trained only on non anomalous images so when it is...
test-img

Asky

deep-learning

encoding

decoding

autoencoder

anomaly-detection

Votes: 0

Answers: 0

How to improve the performance of Isolation forest on time series?

I'm using Isolation Forest on a time series. I'm trying to classify faults in sensor data, but Isolation forest is not functioning quite like I would like. The picture shows how a lot of the jumps are...
test-img

Julian

time-series

outliers

anomaly-detection

Votes: 0

Answers: 1

Latest Answer

The features you use must reflect the kind of anomalies you wish to detect. In your case, you define anomalies as large jumps in values within a short time span. For this, you should transform the inp...
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Jon Nordby

How to know which features causes anomalies while training isolation forest model

I'm training an unsupersived isolation forest model with a dataframe that contains 10 features , the model performs well and detect anomalies. My question is if an anomaly is catched i want to know wh...
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Bacem

machine-learning

unsupervised-learning

anomaly-detection

isolation-forest

Votes: 0

Answers: 1

Latest Answer

SHAP values and the shap library can be used for this. See this answer for an example. After getting the shap values out of the explainer for your datapoints, you can use the waterfall plots to see ho...
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Jon Nordby

Extracting co-anomalies across shared time durations in R

I need to extract co-anomalies from a data-frame which already contains univariate anomalies. # Libraries library(dplyr) library(lubridate) library(stringr) # Create input dataframe DF <- data.fra...
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Brad

dplyr

lubridate

anomaly-detection

Votes: 0

Answers: 1

Latest Answer

You can try this approach using rowwise(): left_join(DF, DF %>% rowwise() %>% filter(any(int_overlaps(Interval, DF$Interval[which(DF$rowID!=rowID & DF$Process == c)]))) %>% mutate...
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langtang

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