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Time-series event-based prediction: an unsupervised learning framework based on genetic programming
journal contribution
posted on 2015-02-13, 11:50 authored by Ahmed Kattan, Syeda FatimaSyeda Fatima, Muhammad ArifIn 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 ¼ fðVaÞja ¼ 0 ng, where each Va is a
time-series vector such that 8Va 2 S; Va ¼ fðxtÞjt ¼ 0 tmaxg where tmax is the size of the
time-series. All Va 2 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 ¼ fðbiÞji ¼ 0 tmaxg,
based on the time-slot t of the target event in each Va training sample. Each bi 2 Bins will
contain a subset of S. For each bi, the proposed framework further classifies its samples into
statistically independent clusters. To achieve this, each bi is treated as an independent
problem where GP is used to evolve programs to extract statistical features from each
bi’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.
History
School
- Science
Department
- Computer Science
Published in
Information SciencesVolume
301Issue
20Pages
99 - 123 (24)Citation
KATTAN, A., FATIMA, S. and ARIF, A., 2015. Time-series event-based prediction: an unsupervised learning framework based on genetic programming. Information Sciences, 301, pp. 99 - 123.Publisher
©Elsevier Inc.Version
- VoR (Version of Record)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Publication date
2015Notes
Closed accessISSN
0020-0255Publisher version
Language
- en