Things on the horizon :)

This is not really a roadmap per se it is more just a collection of ideas at the moment, in no particular order.

UPDATE 20180526 - Skyline itself now seems to write its own roadmap and also seems to be plotting it’s own course. Regardless of any whaffle you may read here.

Further performance improvements and Python-3.5

Further performance improvements where possible with the use of cython. pypy is unfortunately not an overall viable route due to no real support for scipy and limited numpy support. pypy would be a quick win if it were possible, so cython where applicable is the obvious choice.

Continue updating the code base to ensure that everything can be run on >= Python-3.5.x

UPDATE 20180526 - Skyline has not been cooperative here in terms of it’s own roadmap and plotted course.


Functionality to allow the operator to flag false positives, with a view to using machine learning to train Skyline on anomalies. Using the entire data set for training it, perhaps even using an entire namespace to increase the accuracy of the anomaly detection via multiple avenues of analysis.

In progress

See Ionosphere and Development - Ionosphere

UPDATE 20180526 - Ionosphere development, still in progress in small increments

UPDATE 20180715 - Ionosphere stable


Add the ability to inject data into a data set and test a metric, the workflow, algorithms, alerters and the apps themselves,etc.

UPDATE 20180526 - Skyline has not been cooperative here.

Constellations (Updated 20150526 now to be known as Luminosity)

A pure Python implementation of the Oculus functionality, but not necessarily exactly the same, but similar. Calculating in realtime using the redis data and fingerprinting the slope/gradient/alphabet of the last x datapoints of each timeseries on the fly and correlating / finding similarities in the timeseries in terms of patterns. Perhaps some inplementation of Cosine Similarity could be used. A pure Python implementation of Oculus functionality within Skyline would remove the requirement for the additional overheads of ruby and Elasticsearch.

This would allow for the correlations to be determined for any metrics at any point within the FULL_DURATION period.

Help wanted.

UPDATE 20180526 - Skyline HAS been cooperative here and hooked up with Linkedin/luminol to provide Skyline Luminosity and adding back the additional dimension of correlations, it has managed to provide a fairly good root cause analysis dimension to Skyline too. Ticked. Although I still like the app Skyline meme of Constellations, just have to think up or find another cool thing (similarly related) to name Constellations. No doubt in the next roadmap edition, this may be updated with something like, “Skyline and other parties have not been cooperative here.” Take that as ode to yourselves Linkedin, the name was even changed to fit in with paying homeage to your library, however if the aim here is to be accurate the app should be called skyline/paulbourke… Which gives me an idea :)

UPDATE 20180723 - Skyline HAS now been cooperative here and provided itself with data under which some of the Constellations meme roadmap item could be reborn, after being railroaded by Luminol and Luminosity. It was a well liked meme and it was difficult to accept and implement this meme functionality under anything other than Luminosity. However, now Luminosity is implemented, Skyline has once again pushed its own roadmap forward, as now it needs try and learn/compute what cross correlations are now strongly contextually related within the population of cross correlations per anomaly. A lot of cross correlations are purely valid numeric correlations, however within the population of metrics, they often have no contextual correlations to one another, one is not related to the other in any way or form.

It is here Constellations can be reborn by an effort to try and develop a method by which Skyline can learn/compute related metrics in a meaningful manner and hopefully a useful manner. Also try and learn/compute what metrics are never related to each other. Describe constellations and not in the literal sense, constellations can be found in a constellation too. Maps. Map the population, tribes, names, places, stars and know who is who, what is what, who did what, who affects what, who affects what and what affects what. Map it all, somehow. Just thoughts and reflections on v1.2.0 thru v1.2.4, Skyline again has opened itself another avenue of data from some added functionality. Mapping and taming the correlations. This time if possible with no operator training, but simply trying to learn/compute from all the available data. Perhaps it can ask the operator to review what it figured out and get their opinion on it. Who knows where this would go? Through a lot of time undoubtedly, that much is certain.

Skyline and NASA/FITS data

There is a Skyline module in development that is specifically designed for analysing Kepler FITS timeseries data. Although it specifically focus at extracting lightcurve data from the Kepler llc.fits data files, that in itself has added the functionality to convert any relevant FITS based data file that has a timeseries element in it. This should extend to other data types that the FITS data format (Flexible Image Transport System) handles:

  • Much more than just another image format (such as JPEG or GIF)

  • Used for the transport, analysis, and archival storage of scientific data sets

  • Multi-dimensional arrays: 1D spectra, 2D images, 3D+ data cubes

  • Tables containing rows and columns of information

  • Header keywords provide descriptive information about the data

It should be possible for Skyline to be able to ingest any FITS data file or ingest an image file from a FITS file and feed it to scikit-image, etc.

UPDATE 20180526 - Skyline and other parties have not been cooperative here.


Last year NuPIC was assessed in terms of use in Internet advertising for an additional monitoring dimension. Although the assessment was fairly brief, it’s predictive capabilities were quite impressive. Working with a 14 month data set for one advertising service it was able to predict the service’s ad_requests per 10 minutes for the 14 month period remarkably well given the nature of the specific domain. The result was a little peaky, with a few large peaks. Evaluation of the event streams from the timeframes around the large peaks in question, identified known causes for those peaks, with a “noise filter” applied to exclude > x to reduce those now known few noisy events and the result were indeed remarkable. In fact NuPIC predicted our requests for 14 months within the bounds of ~5% error margin and when it made and error, it learned.

Truly remarkable given this is adtech data, 6.5 million request per minute peaks, low troughs with sparse seasonalities (if there is such a thing) at times. NuPIC and the Cortical Learning Algorithm are really amazing, if not a little difficult upfront.

NuPIC predictions and Graphite timeseries

Feeding NuPIC prediction data back into Graphite helps our own Neocortex to visually analyse the results too (data courtesy of Coull).

Original requests timeseries

Original requests timeseries data

Original requests timeseries data

NuPIC predicted timeseries

NuPIC predicted data

NuPIC predicted data

percentage error - positive and negative

  • positive - under predicted (we did more than predicted)

  • negative - over predicted (we did less than predicted)


Drop the noisy for an average representation

NuPIC percentage error

NuPIC percentage error


Real data, NuPIC predictions and percentage error (on the 2nd unticked y axis as above)

Real data, NuPIC predictions and percentage error

Real data, NuPIC predictions and percentage error

Quite amazing. It a not beyond the realms of possibility to have a Horizon feeding specific metrics to various NuPIC HTM Cortical Learning Algorithms models…

UPDATE 20180526 - Skyline and other parties have not been cooperative here.

Update the NAB Scoreboard

Look at the automated running of the Numenta Anomaly Benchmark (NAB) data and frequently determine the Standard Profile, Reward Low FP and Reward Low FN scores (metrics). This will only aid and improve the evaluation of any additional algorithms, methods or techniques that are added or applied to Skyline in the future, e.g:

Automated NAB benchmark metrics would be a nice thing to have :)

Update 20170225: Skyline can no longer necessarily be NAB-ed due to it now functioning in multiple temporal resolutions as a whole.

UPDATE 20180526 - Skyline and other parties have not been cooperative here.

UPDATE 20190412 - New Skyline scores were added to NAB see:

Machine learning

Bring additional dimensions of machine learning capabilities into Skyline, too many avenues to mention…

UPDATE 20180526 - Skyline and other parties have not been cooperative here.


Investigate if any elements could benefit performance wise from being implemented in Grumpy - go running Python - there may be some mileage in using go instead of multiprocessing in some cases, algorithms, ionosphere, tsfresh chunks to go in extract_features rather than multiprocessing, scope for investigation.

UPDATE 20180526 - Skyline and other parties have not been cooperative here. So at least one thing on the roadmap was done. I guess that is progress :)