
Anomaly Detection of Time Series Data Using Machine Learning and Deep Learning - myth_drannon
https://www.xenonstack.com/blog/anomaly-detection-of-time-series-data-using-machine-learning-deep-learning
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baldeagle
TLDR: This is a high level overview of all the terms in the topic (Anomaly,
Time Series, Deep Learning). If you have a passing familiarity with the
problem space, there is nothing new here. There is an ad for xenonstack
services at the bottom.

~~~
nerdponx
It taught me about the AnomalyDetection R package, which was nice.

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VHRanger
> This is because the oscillations are dependable upon the business cycle.

No, no, no, no. The "business cycle" is a misnomer; it's not a cycle. It
doesn't fit any descriptions of a "cycle" except for the fact that it
sometimes goes up and sometimes goes down.

Saying that it's a cycle implies you can time downward trends like crashes or
predict their intensity, which, anyone in finance or maroeconomics will tell
you is impossible

~~~
krona
_Saying that it 's a cycle implies you can time downward trends like crashes
or predict their intensity, which, anyone in finance or maroeconomics will
tell you is impossible_

Just because something is hard doesn't mean it's impossible; You can predict
anything with a degree of certainty. Then there are things like uncertainty
and radical (Knightian) uncertainty.

Any chartered actuary would tell you the same if you could afford one, and
they will know quite a bit about business cycles and how to model them, too.

~~~
VHRanger
Except you won't in macroeconomics. The simple problem in macroeconomics is
lack of data -- history happened only once so you can't test counterfactuals.

You can try to build a model, but the confidence intervals for the next big
crash are going to be either useless (way too wide) or bullshit if what you're
trying to predict is the next market crash. Building a classifier around it
machine learning style will not fundamentally help, either.

You can try it: here is an open source, serious, full scale macroeconomic
model by the NY Fed [1]. Try to predict the next large crash with it and
report back

[1] [https://github.com/FRBNY-DSGE/DSGE.jl](https://github.com/FRBNY-
DSGE/DSGE.jl)

~~~
neffy
This would be the same macro-economic theory that even macro-economists will
now grant is fundamentally flawed.

The rest is left as an exercise to the reader.

------
Xcelerate
If you're interested in more research level techniques, check out some of the
work that's been done using minimum description length (MDL). It basically
seeks to minimize the size of the model plus the size of the residual data
(that the model doesn't predict) and uses Huffman compression as a proxy for
the Kolmogorov complexity of a string:

"Discovering the intrinsic cardinality and dimensionality of time series using
MDL"
[https://pdfs.semanticscholar.org/2049/50b3cd9cf2eef52f957df6...](https://pdfs.semanticscholar.org/2049/50b3cd9cf2eef52f957df626e01cad7adcef.pdf)

A few papers that build on that one show how to incorporate anomaly detection
(in the sense that the anomaly is incompressible).

~~~
boltzmannbrain
And more on research-level techniques:

Ahmad et al. "Unsupervised real-time anomaly detection for streaming data":
[http://www.sciencedirect.com/science/article/pii/S0925231217...](http://www.sciencedirect.com/science/article/pii/S0925231217309864)

Anomaly detection benchmark (dataset and evaluation):
[https://github.com/numenta/NAB](https://github.com/numenta/NAB)

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Omnipresent
Since this article is very high level, are there other resources that show how
to apply some (or all) of these topics in actual code or on a sample set of
data?

~~~
myth_drannon
[http://machinelearningmastery.com/blog/](http://machinelearningmastery.com/blog/)
has some good tutorials on time series and ML in general

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gargravarr
The first few words of this title sound way too sci-fi. Very disappointed when
I read the whole thing together.

Edit: supposed to be a light-hearted observation, I haven't read the article!

