Anomaly detection and motif discovery in symbolic representations of time series

The advent of the Big Data hype and the consistent recollection of event logs and real-time data from sensors, monitoring software and machine configuration has generated a huge amount of time-varying data in about every sector of the industry. Rule-based processing of such data has ceased to be relevant in many scenarios where anomaly detection and pattern mining have to be entirely accomplished by the machine. Since the early 2000s, the de-facto standard for representing time series has been the Symbolic Aggregate approXimation (SAX).In this document, we present a few algorithms using this representation for anomaly detection and motif discovery, also known as pattern mining, in such data. We propose a benchmark of anomaly detection algorithms using data from Cloud monitoring software.

GUIDE: Unifying Evolutionary Engines through a Graphical User Interface

Many kinds of Evolutionary Algorithms (EAs) have been described in the literature since the last 30 years. However, though most of them share a common structure, no existing software package allows the user to actually shift from one model to another by simply changing a few parameters, e.g. in a single window of a Graphical User Interface. This paper presents GUIDE, a Graphical User Interface for DREAM Experiments that, among other user-friendly features, unifies all kinds of EAs into a single panel, as far as evolution parameters are concerned. Such a window can be used either to ask for one of the well known ready-to-use algorithms, or to very easily explore new combinations that have not yet been studied. Another advantage of grouping all necessary elements to describe virtually all kinds of EAs is that it creates a fantastic pedagogic tool to teach EAs to students and newcomers to the field.

Evolving localizations in reaction-diffusion cellular automata

We consider hexagonal cellular automata with immediate cell neighbourhood and three cell-states. Every cell calculates its next state depending on the integral representation of states in its neighbourhood, i.e. how many neighbours are in each one state. We employ evolutionary algorithms to breed local transition functions that support mobile localizations (gliders), and characterize sets of the functions selected in terms of quasi-chemical systems. Analysis of the set of functions evolved allows to speculate that mobile localizations are likely to emerge in the quasi-chemical systems with limited diffusion of one reagent, a small number of molecules is required for amplification of travelling localizations, and reactions leading to stationary localizations involve relatively equal amount of quasi-chemical species. Techniques developed can be applied in cascading signals in nature-inspired spatially extended computing devices, and phenomenological studies and classification of non-linear discrete systems.

Stochastic Optimization Algorithms

When looking for a solution, deterministic methods have the enormous advantage that they do find global optima. Unfortunately, they are very CPU-intensive, and are useless on untractable NP-hard problems that would require thousands of years for cutting-edge computers to explore. In order to get a result, one needs to revert to stochastic algorithms, that sample the search space without exploring it thoroughly. Such algorithms can find very good results, without any guarantee that the global optimum has been reached; but there is often no other choice than using them. This chapter is a short introduction to the main methods used in stochastic optimization.