The Intelligent Forecasting Model of Time Series
Automation, Control and Intelligent Systems
Volume 1, Issue 4, August 2013, Pages: 90-98
Received: Jul. 16, 2013; Published: Aug. 10, 2013
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Sonja Pravilović, Montenegro Business School, "Mediterranean" University, Podgorica, Montenegro
Annalisa Appice, Dipartimento di Informatica, Università degli Studi di Bari Aldo Moro, Bari, Italy
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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.
Time Series Analysis, Arima, Auto. Arima, Lag. Arima
To cite this article
Sonja Pravilović, Annalisa Appice, The Intelligent Forecasting Model of Time Series, Automation, Control and Intelligent Systems. Vol. 1, No. 4, 2013, pp. 90-98. doi: 10.11648/j.acis.20130104.12
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