Time-series event-based prediction: An unsupervised learning framework based on genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Ahmed Kattan and Shaheen Fatima and Muhammad Arif",
  title =        "Time-series event-based prediction: An unsupervised
                 learning framework based on genetic programming",
  journal =      "Information Sciences",
  volume =       "301",
  pages =        "99--123",
  year =         "2015",
  ISSN =         "0020-0255",
  DOI =          "doi:10.1016/j.ins.2014.12.054",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0020025515000067",
  abstract =     "In this paper, we propose an unsupervised learning
                 framework based on Genetic Programming (GP) to predict
                 the position of any particular target event (defined by
                 the user) in a time-series. GP is used to automatically
                 build a library of candidate temporal features. The
                 proposed framework receives a training set S = { ( V a
                 ) | a = a ... n } , where each V a is a time-series
                 vector such that forall V a elementof S , V a = { ( x t
                 ) | t = a ... t max } where t max is the size of the
                 time-series. All V a elementof S are assumed to be
                 generated from the same environment. The proposed
                 framework uses a divide-and-conquer strategy for the
                 training phase. The training process of the proposed
                 framework works as follow. The user specifies the
                 target event that needs to be predicted (e.g., Highest
                 value, Second Highest value,..., etc.). Then, the
                 framework classifies the training samples into
                 different Bins, where Bins = { ( b i ) | i = a ... t
                 max } , based on the time-slot t of the target event in
                 each V a training sample. Each b i elementof Bins will
                 contain a subset of S. For each b i , the proposed
                 framework further classifies its samples into
                 statistically independent clusters. To achieve this,
                 each b i is treated as an independent problem where GP
                 is used to evolve programs to extract statistical
                 features from each b i 's members and classify them
                 into different clusters using the K-Means algorithm. At
                 the end of the training process, GP is used to build an
                 `event detector' that receives an unseen time-series
                 and predicts the time-slot where the target event is
                 expected to occur. Empirical evidence on artificially
                 generated data and real-world data shows that the
                 proposed framework significantly outperforms standard
                 Radial Basis Function Networks, standard GP system,
                 Gaussian Process regression, Linear regression, and
                 Polynomial Regression.",
  keywords =     "genetic algorithms, genetic programming, Unsupervised
                 learning, Time-series, K-Means, Prediction, Event

Genetic Programming entries for Ahmed Kattan Shaheen Fatima Muhammad Arif