How can i implement callback parameter in fit moder Autoencoder ?
There is not parameter.

from keras.callbacks.callbacks import EarlyStopping
cb_earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=0, verbose=0,
mode='auto', baseline=None, restore_best_weights=False)
pyod_model.fit(scaler, callbacks=[cb_earlystop])

TypeError: fi

trex@beast:~/Development/siren/sentinl$ grep -rne "indices\"" docs/
docs/Watcher-Examples.md:227:          "indices": [
docs/Watcher-Examples.md:268:          "indices": [
docs/HOWTO-Alerts-in-Nagios-NRDP.md:157:        "indices": [

On the UCR Matrix Profile site, they have a growing list of time series questions that can be solved by computing the matrix profile. The PDF can be found here

It would be interesting to begin compiling a STUMPY examples that reproduces the solutions to those questions below (including data sources).

Datastream.io is not working because some of the library dependencies are not set to the correct version (tornado and elasticsearch in my case).
Here is the pip freeze (python3.5) of the fully working datastream.io:

`bokeh==1.3.0
dateparser==0.7.1
-e git+https://github.com/MentatInnovations/datastream.io@a243b89ec3c4e06473b5004c498c472ffd37ead2#egg=dsio
elasticsearch==5.5.3
Jinja2==2.10.1

Please throw a warning message to the user when automatically modifying parameters or algorithms. Doing this silently makes it extremely difficulty to debug and fine-tune.

https://github.com/linkedin/luminol/blob/42e4ab969b774ff98f902d064cb041556017f635/src/luminol/algorithms/anomaly_detector_algorithms/bitmap_detector.py#L60-L73

https://github.com/linkedin/luminol/blob/42e4ab969b774ff98f902

nowadays, docs with rarely interpretation is difficult to understand the algorithm,
Such as bayes rule sets and other Underdogs have little references.
If can provide a common introduce in interface level may be good for programmers
who have little information of algorithm to start

Some models (e.g. those including moving average operation) may return series with NaN on the two ends. We should add a warning or note in the docstrings (and hence docs) to highlight this.

It would be great to support these optional header fields according to ENVI documentation:

http://www.exelisvis.com/docs/EnterOptionalHeaderInformation.html#Set3

It's not going to be easy with the current memmap implementation.

As the current capabilities of PyNomaly are solidified and new capabilities added, it would be beneficial to have dedicated documentation that is hosted and available to users outside of the readme.