Paris Machine Learning Meetup #5 Season 2, Time Series and FinTech, Adversarial Algos

Paris Machine Learning Meetup #5 Season 2 is tonight and it will be was a joint meetup with the Paris Financial Engineering meetup. Our current meetup membership has risen to 1700+ members making it the largest Machine Learning Meetup outside the U.S. (and top 6 worldwide).

We have some growth issues, this is why we are trying a new location today. Our sponsor and host is the Maltem Consulting Group.

• If you want to do a presentation for one of our next meetups, we have set up a form (it's in French for the moment), it is here: http://goo.gl/forms/7ogXzchTfn
• If you want to receive our low frequency letter (one per month max), you need to let us know us here: http://goo.gl/forms/mqFB0e3SwM

Let us note that we also have about 600 professionals listed in the LinkedIn Paris Machine Learning group (this is where the jobs should be posted). The meetup hashtag is #MLParis (all the previous speakers and sponsors on Twitter, are on this list). The video below will restransmit the meetup. It starts at 7:00PM Paris time. All the presentations are already available below and will be shortly on the meetup archives:

      Warning: It is very likely that all the talks will be in French. However, all the slides are listed below and are in English.  

The program focused on times series and specifically on adversarial algorithms i.e algorithms that have to discover time changing patterns when the other side also have similar probing capabilities. We had the example of Botnet detections using a mix of unsupervised learning and metric evaluation in one presentation and the decomposition of financial series using a new metric in order to different correlated movements between series and those linked to specific distributions. We also had the presentation of a simple API that can easily provide low latency financial data as well as an historically remarkable invariant trend over the years in financial data.

Au programme, un focus sur l'apport du machine learning appliqué aux séries temporelles et à la finance quantitative:

• James Nacass (www.moneypush.com) nous parlera de son API de trading  www.bigdtrade.com et fera une démo BigDtrade, A simple API to trade on the markets /"L'API la plus simple pour trader sur les marchés financiers."
• Anaël Bonneton (Agence Nationale de la Sécurité des systèmes d'information) Botnet detection with time series decision trees. Anaël nous parlera d'un modèle de classification de séries temporelles appliqué à la détection de botNet.

• Gautier Marti (Hellebore Capital), "How to cluster random walks? - Application to the Credit Default Swap market"

In this talk, we present a novel non-parametric approach for clustering high-dimensional Markov processes by splitting apart dependency and distribution information without losing any. This approach is able to recover any generative-random-walk-model ground-truth unlike a straightforward application of clustering techniques to time series. 
We have applied the method to a large financial market dataset of  time series, the CDS market prices of more than 600 companies over the last 8 years through the global financial crisis. Results are compared both with the basic workflow clusters and with an expert's classification. We illustrate their clustering dissimilarities using the HCMapper, a bespoke data visualisation designed to highlight clustering mismatches.
Some experiment code and internal research are released at http://www.datagrapple.com/Tech on an ongoing basis. 

• Yves Lempérière (Capital Fund Management) "200 years of trend following"

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