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Author: Jan Kostrzewa

Affiliation: Instytut Podstaw Informatyki Polskiej Akademii Nauk, Poland

Keyword(s): Time Series, Forecasting, Data Mining, Subseries, Clustering, Periodic Pattern.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer-Supported Education ; Data Manipulation ; Domain Applications and Case Studies ; Enterprise Information Systems ; Fuzzy Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Industrial, Financial and Medical Applications ; Methodologies and Methods ; Neural Based Data Mining and Complex Information Processing ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Supervised and Unsupervised Learning ; Support Vector Machines and Applications ; Theory and Methods

Abstract: Time series forecasting have attracted a great deal of attention from various research communities. One of the method which improves accuracy of forecasting is time series clustering. The contribution of this work is a new method of clustering which relies on finding periodic pattern by splitting the time series into two subsequences (clusters) with lower potential error of prediction then whole series. Having such subsequences we predict their values separately with methods customized to the specificities of the subsequences and then merge results according to the pattern and obtain prediction of original time series. In order to check efficiency of our approach we perform analysis of various artificial data sets. We also present a real data set for which application of our approach gives more then 300% improvement in accuracy of prediction. We show that in artificially created series we obtain even more pronounced accuracy improvement. Additionally our approach can be use to nois e filtering. In our work we consider noise of a periodic repetitive pattern and we present simulation where we find correct series from data where 50% of elements is random noise. (More)

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Paper citation in several formats:
Kostrzewa, J. (2015). Time Series Forecasting using Clustering with Periodic Pattern. In Proceedings of the 7th International Joint Conference on Computational Intelligence (ECTA 2015) - NCTA; ISBN 978-989-758-157-1, SciTePress, pages 85-92. DOI: 10.5220/0005586900850092

@conference{ncta15,
author={Jan Kostrzewa.},
title={Time Series Forecasting using Clustering with Periodic Pattern},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence (ECTA 2015) - NCTA},
year={2015},
pages={85-92},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005586900850092},
isbn={978-989-758-157-1},
}

TY - CONF

JO - Proceedings of the 7th International Joint Conference on Computational Intelligence (ECTA 2015) - NCTA
TI - Time Series Forecasting using Clustering with Periodic Pattern
SN - 978-989-758-157-1
AU - Kostrzewa, J.
PY - 2015
SP - 85
EP - 92
DO - 10.5220/0005586900850092
PB - SciTePress

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