The Intelligent Forecasting Model of Time Series

Sonja Pravilović; Annalisa Appice

doi:doi:10.11648/j.acis.20130104.12

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The Intelligent Forecasting Model of Time Series

Sonja Pravilović, Annalisa Appice

Published in Automation, Control and Intelligent Systems (Volume 1, Issue 4)

Received: 16 July 2013 Published: 10 August 2013

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Abstract

Automatic forecasts of univariate time series are largely demanded in business and science. In this paper, we investigate the forecasting task for geo-referenced time series. We take into account the temporal and spatial dimension of time series to get accurate forecasting of future data. We describe two algorithms for forecasting which ARIMA models. The first is designed for seasonal data and based on the decomposition of the time series in seasons (temporal lags). The ARIMA model is jointly optimized on the temporal lags. The second is designed for geo-referenced data and based on the evaluation of a time series in a neighborhood (spatial lags). The ARIMA model is jointly optimized on the spatial lags. Experiments with several time series data investigate the effectiveness of these temporal- and spatial- aware ARIMA models with respect to traditional one.

Published in	Automation, Control and Intelligent Systems (Volume 1, Issue 4)
DOI	10.11648/j.acis.20130104.12
Page(s)	90-98
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2013. Published by Science Publishing Group

Keywords

Time Series Analysis, Arima, Auto. Arima, Lag. Arima

References

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[4]	D.W. Bunn, "Combining forecasts", European Journal of the Operational Reseach. 33, 1988, pp. 223-229.
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[6]	S.L. Ho, "The use of ARIMA models for reliability forecasting and analyses", Computers and industrial engineering. 35 (1-2), 1998. pp. 213-216.
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[11]	R.J. Hyndman, "Forecasting Functions for Time Series", R package version 1.11, http://CRAN.R-project.org/package =forecasting , 2008.
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[16]	O. Ohashi, L. Torgo, Wind speed forecasting using spatio-temporal indicators. In L. D. Raedt, C. Bessiere, D. Dubois, P. Doherty, P. Frasconi, F. Heintz, P. J. F. Lucas, editors, 20th European Conference on Artiﬁcial Intelligence. Including Prestigious Applications of Artiﬁcial Intelligence (PAIS-2012), SystemDemonstrations Track, volume 242 of Frontiers in Artiﬁcial Intelligence and Applications, pp. 975–980. IOS Press, 2012.
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Sonja Pravilović, Annalisa Appice. (2013). The Intelligent Forecasting Model of Time Series. Automation, Control and Intelligent Systems, 1(4), 90-98. https://doi.org/10.11648/j.acis.20130104.12

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ACS Style

Sonja Pravilović; Annalisa Appice. The Intelligent Forecasting Model of Time Series. Autom. Control Intell. Syst. 2013, 1(4), 90-98. doi: 10.11648/j.acis.20130104.12

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AMA Style

Sonja Pravilović, Annalisa Appice. The Intelligent Forecasting Model of Time Series. Autom Control Intell Syst. 2013;1(4):90-98. doi: 10.11648/j.acis.20130104.12

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@article{10.11648/j.acis.20130104.12,
  author = {Sonja Pravilović and Annalisa Appice},
  title = {The Intelligent Forecasting Model of Time Series},
  journal = {Automation, Control and Intelligent Systems},
  volume = {1},
  number = {4},
  pages = {90-98},
  doi = {10.11648/j.acis.20130104.12},
  url = {https://doi.org/10.11648/j.acis.20130104.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20130104.12},
  abstract = {Automatic forecasts of univariate time series are largely demanded in business and science. In this paper, we investigate the forecasting task for geo-referenced time series. We take into account the temporal and spatial dimension of time series to get accurate forecasting of future data. We describe two algorithms for forecasting which ARIMA models. The first is designed for seasonal data and based on the decomposition of the time series in seasons (temporal lags). The ARIMA model is jointly optimized on the temporal lags. The second is designed for geo-referenced data and based on the evaluation of a time series in a neighborhood (spatial lags). The ARIMA model is jointly optimized on the spatial lags. Experiments with several time series data investigate the effectiveness of these temporal- and spatial- aware ARIMA models with respect to traditional one.},
 year = {2013}
}

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TY  - JOUR
T1  - The Intelligent Forecasting Model of Time Series
AU  - Sonja Pravilović
AU  - Annalisa Appice
Y1  - 2013/08/10
PY  - 2013
N1  - https://doi.org/10.11648/j.acis.20130104.12
DO  - 10.11648/j.acis.20130104.12
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AB  - Automatic forecasts of univariate time series are largely demanded in business and science. In this paper, we investigate the forecasting task for geo-referenced time series. We take into account the temporal and spatial dimension of time series to get accurate forecasting of future data. We describe two algorithms for forecasting which ARIMA models. The first is designed for seasonal data and based on the decomposition of the time series in seasons (temporal lags). The ARIMA model is jointly optimized on the temporal lags. The second is designed for geo-referenced data and based on the evaluation of a time series in a neighborhood (spatial lags). The ARIMA model is jointly optimized on the spatial lags. Experiments with several time series data investigate the effectiveness of these temporal- and spatial- aware ARIMA models with respect to traditional one.
VL  - 1
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Author Information

Sonja Pravilović
Annalisa Appice

Dipartimento di Informatica, Università degli Studi di Bari Aldo Moro, Bari, Italy

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APA Style

Sonja Pravilović, Annalisa Appice. (2013). The Intelligent Forecasting Model of Time Series. Automation, Control and Intelligent Systems, 1(4), 90-98. https://doi.org/10.11648/j.acis.20130104.12

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ACS Style

Sonja Pravilović; Annalisa Appice. The Intelligent Forecasting Model of Time Series. Autom. Control Intell. Syst. 2013, 1(4), 90-98. doi: 10.11648/j.acis.20130104.12

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AMA Style

Sonja Pravilović, Annalisa Appice. The Intelligent Forecasting Model of Time Series. Autom Control Intell Syst. 2013;1(4):90-98. doi: 10.11648/j.acis.20130104.12

Copy | Download

@article{10.11648/j.acis.20130104.12,
  author = {Sonja Pravilović and Annalisa Appice},
  title = {The Intelligent Forecasting Model of Time Series},
  journal = {Automation, Control and Intelligent Systems},
  volume = {1},
  number = {4},
  pages = {90-98},
  doi = {10.11648/j.acis.20130104.12},
  url = {https://doi.org/10.11648/j.acis.20130104.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20130104.12},
  abstract = {Automatic forecasts of univariate time series are largely demanded in business and science. In this paper, we investigate the forecasting task for geo-referenced time series. We take into account the temporal and spatial dimension of time series to get accurate forecasting of future data. We describe two algorithms for forecasting which ARIMA models. The first is designed for seasonal data and based on the decomposition of the time series in seasons (temporal lags). The ARIMA model is jointly optimized on the temporal lags. The second is designed for geo-referenced data and based on the evaluation of a time series in a neighborhood (spatial lags). The ARIMA model is jointly optimized on the spatial lags. Experiments with several time series data investigate the effectiveness of these temporal- and spatial- aware ARIMA models with respect to traditional one.},
 year = {2013}
}

Copy | Download

TY  - JOUR
T1  - The Intelligent Forecasting Model of Time Series
AU  - Sonja Pravilović
AU  - Annalisa Appice
Y1  - 2013/08/10
PY  - 2013
N1  - https://doi.org/10.11648/j.acis.20130104.12
DO  - 10.11648/j.acis.20130104.12
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 90
EP  - 98
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20130104.12
AB  - Automatic forecasts of univariate time series are largely demanded in business and science. In this paper, we investigate the forecasting task for geo-referenced time series. We take into account the temporal and spatial dimension of time series to get accurate forecasting of future data. We describe two algorithms for forecasting which ARIMA models. The first is designed for seasonal data and based on the decomposition of the time series in seasons (temporal lags). The ARIMA model is jointly optimized on the temporal lags. The second is designed for geo-referenced data and based on the evaluation of a time series in a neighborhood (spatial lags). The ARIMA model is jointly optimized on the spatial lags. Experiments with several time series data investigate the effectiveness of these temporal- and spatial- aware ARIMA models with respect to traditional one.
VL  - 1
IS  - 4
ER  -

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