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Position and Communication Papers of the 16th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 26

Impact of time series clustering on fuel sales prediction results

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DOI: http://dx.doi.org/10.15439/2021F129

Citation: Position and Communication Papers of the 16th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 26, pages 13–21 ()

Full text

Abstract. We investigated the impact of data clustering in the process of predicting demand. We checked different ways of adding information about similar datasets to the forecasting process and we grouped measurements in multiple ways. The experiments were executed on 50 time series describing fuels sales. We used the XGBoost algorithm and some typical time series forecasting methods. We showed a case study for two datasets and we discussed the practical usage of the tested solutions. The results showed that the solution which used XGBoost model utilising data gathered from all available petrol stations, in general, worked the best.

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