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Lattice-Based Spatio-temporal Prediction

Samulevicius, Saulius and Pitarch, Yoann and Pedersen, Torben Bach Lattice-Based Spatio-temporal Prediction. (2014) In: 18th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2014), 15 September 2014 - 17 September 2014 (Gdynia, Poland).

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Official URL: http://dx.doi.org/10.1016/j.procs.2014.08.130

Abstract

With the rapidly increasing deployment of sensor networks, large amounts of time series data are generated. One of the main challenges when dealing with such data is performing accurate predictions in order to address a broad class of application problems, ranging from mobile broadband network (MBN) optimization to preventive maintenance. To this end, time series prediction has been widely addressed by the statistics community. Nevertheless, such approaches fail in performing well when the data are more context-dependent than history-dependent. In this paper, we investigate how latent attributes can be built upon the time series in order to define a spatio-temporal context for predictions. Moreover, such attributes are often hierarchical, leading to multiple potential contexts at different levels of granularity for performing a given prediction. In support of this scenario, we propose the Lattice-Based Spatio-Temporal Ensemble Prediction (LBSTEP) approach, which allows modeling the problem as a multidimensional spatio-temporal prediction. Given an ensemble prediction model, we propose a solution for determining the most appropriate spatio-temporal context that maximizes the global prediction metrics of a set of the time series. LBSTEP is evaluated with a real-world MBN dataset, which exemplifies the intended general application domain of time series data with a strong spatio-temporal component. The experimental results shows that the proposed contextual and multi-granular view of the prediction problem is effective, in terms of both several optimization metrics and the model calculation.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Thanks to Elsevier editor. This papers appears in Procedia Computer Science 35 The definitive version is available at: http://www.sciencedirect.com The original PDF of the article can be found at : http://www.sciencedirect.com/science/article/pii/S1877050914010953
HAL Id:hal-01399866
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - INPT (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UPS (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Aalborg University (DENMARK)
Laboratory name:
Statistics:download
Deposited By: IRIT IRIT
Deposited On:07 Nov 2016 14:35

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