Which between a DL autoencoder, Twitter STL+ESD and Numenta HBM is more state-of-the-art for anomaly...
$begingroup$
I aim to develop a generic algorithm to detect abnormalities in time series (ideally accepting multivariate series, and the presence of some noise). The aspects which I am considering for comparing these, are mainly:
- Seasonal variations
- Amplitude variations on periods.
- Period length variations.
- Delays (missing data) between periods
- Single points variations
I have read about many algorithms, and the ones which I found more state of the art, are these:
- (year?) Using Deep learning autoencoders & decide period lengths via Fast Fourier Transform or ACF. ----- Article ----- Implementation
- (Apr 2017) Twitter STL + ESD algorithm. ----- Article ----- Implementation.
- (Nov 2017) Numenta HTM. Article. ---- Implementation
Numenta also provides in this repository a benchmark between algorithms for anomaly detection. However, (1) I do not know if this benchmark englobes all main methods for this task, and (2) I am not sure if the way they did the benchmark include all the 5 abnormalities I mentioned.
Also, I saw there are more algorithms, like the ones developed here, and I have seen the classification of anomaly detection in basic, statistical and probabilistic ways here, but there is way too much information around for me to figure out my next question.
My question is:
Which algorithm / minimal algorithms combination will [best] detect the kinds of abnormalities aforementioned and why?
anomaly-detection
asked Nov 12 '18 at 13:05
freesoulfreesoul
84
$endgroup$
add a comment |
$begingroup$
I aim to develop a generic algorithm to detect abnormalities in time series (ideally accepting multivariate series, and the presence of some noise). The aspects which I am considering for comparing these, are mainly:
- Seasonal variations
- Amplitude variations on periods.
- Period length variations.
- Delays (missing data) between periods
- Single points variations
I have read about many algorithms, and the ones which I found more state of the art, are these:
- (year?) Using Deep learning autoencoders & decide period lengths via Fast Fourier Transform or ACF. ----- Article ----- Implementation
- (Apr 2017) Twitter STL + ESD algorithm. ----- Article ----- Implementation.
- (Nov 2017) Numenta HTM. Article. ---- Implementation
Numenta also provides in this repository a benchmark between algorithms for anomaly detection. However, (1) I do not know if this benchmark englobes all main methods for this task, and (2) I am not sure if the way they did the benchmark include all the 5 abnormalities I mentioned.
Also, I saw there are more algorithms, like the ones developed here, and I have seen the classification of anomaly detection in basic, statistical and probabilistic ways here, but there is way too much information around for me to figure out my next question.
My question is:
Which algorithm / minimal algorithms combination will [best] detect the kinds of abnormalities aforementioned and why?
anomaly-detection
asked Nov 12 '18 at 13:05
freesoulfreesoul
84
$endgroup$
add a comment |
$begingroup$
I aim to develop a generic algorithm to detect abnormalities in time series (ideally accepting multivariate series, and the presence of some noise). The aspects which I am considering for comparing these, are mainly:
- Seasonal variations
- Amplitude variations on periods.
- Period length variations.
- Delays (missing data) between periods
- Single points variations
I have read about many algorithms, and the ones which I found more state of the art, are these:
- (year?) Using Deep learning autoencoders & decide period lengths via Fast Fourier Transform or ACF. ----- Article ----- Implementation
- (Apr 2017) Twitter STL + ESD algorithm. ----- Article ----- Implementation.
- (Nov 2017) Numenta HTM. Article. ---- Implementation
Numenta also provides in this repository a benchmark between algorithms for anomaly detection. However, (1) I do not know if this benchmark englobes all main methods for this task, and (2) I am not sure if the way they did the benchmark include all the 5 abnormalities I mentioned.
Also, I saw there are more algorithms, like the ones developed here, and I have seen the classification of anomaly detection in basic, statistical and probabilistic ways here, but there is way too much information around for me to figure out my next question.
My question is:
Which algorithm / minimal algorithms combination will [best] detect the kinds of abnormalities aforementioned and why?
anomaly-detection
asked Nov 12 '18 at 13:05
freesoulfreesoul
84
$endgroup$
I aim to develop a generic algorithm to detect abnormalities in time series (ideally accepting multivariate series, and the presence of some noise). The aspects which I am considering for comparing these, are mainly:
- Seasonal variations
- Amplitude variations on periods.
- Period length variations.
- Delays (missing data) between periods
- Single points variations
I have read about many algorithms, and the ones which I found more state of the art, are these:
- (year?) Using Deep learning autoencoders & decide period lengths via Fast Fourier Transform or ACF. ----- Article ----- Implementation
- (Apr 2017) Twitter STL + ESD algorithm. ----- Article ----- Implementation.
- (Nov 2017) Numenta HTM. Article. ---- Implementation
Numenta also provides in this repository a benchmark between algorithms for anomaly detection. However, (1) I do not know if this benchmark englobes all main methods for this task, and (2) I am not sure if the way they did the benchmark include all the 5 abnormalities I mentioned.
Also, I saw there are more algorithms, like the ones developed here, and I have seen the classification of anomaly detection in basic, statistical and probabilistic ways here, but there is way too much information around for me to figure out my next question.
My question is:
Which algorithm / minimal algorithms combination will [best] detect the kinds of abnormalities aforementioned and why?
anomaly-detection
anomaly-detection
asked Nov 12 '18 at 13:05
freesoulfreesoul
84
asked Nov 12 '18 at 13:05
freesoulfreesoul
84
edited 4 mins ago
freesoul
asked Nov 12 '18 at 13:05
freesoulfreesoul
84
asked Nov 12 '18 at 13:05
freesoulfreesoul
84
asked Nov 12 '18 at 13:05
freesoulfreesoul
84
84
add a comment |
add a comment |
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