Impact of time series clustering on fuel sales prediction results

• Autorzy: Henzel, Joanna , Sikora, Marek , Bularz, Jakub
• Konferencja: Federated Conference on Computer Science and Information Systems (16 ; 02-05.09.2021 ; online)
• Języki publikacji: EN

Abstrakty

EN

The purpose of the paper is to check 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 the measurements in multiple ways. The experiments were executed on 50 time series describing fuels sales (gasoline and diesel sales) on 25 petrol stations from an international company. We described the data preparation process and feature extraction process. In the 9 presented experiments, we used the XGBoost algorithm and some typical time series forecasting methods (ARIMA, moving average). 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 and it outperformed more advanced approaches as well as typical time series methods.

Słowa kluczowe

PL

klastrowanie danych; predykcja popytu; sprzedaż paliw; szereg czasowy

EN

data clustering; demand prediction; fuel sales; time series

• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2021
• Tom: Vol. 26
• Strony: 13--21
• Opis fizyczny: Bibliogr. 23 poz., wykr.

Twórcy

autor

Henzel, Joanna

• Department of Computer Networks and System, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland,

autor

Sikora, Marek

• Department of Computer Networks and System, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland,

autor

Bularz, Jakub

• FuelPrime Department, AIUT Ltd., Wyczółkowskiego 113, 44-109 Gliwice, Poland,

Bibliografia

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Uwagi

1. This research was realised in co-operation with FuelPrime Department in AIUT Ltd. and was partially supported by the European Union through the European Social Fund (grant POWR.03.05.00-00-Z305).

2. Preface

3. Session: 15th International Symposium Advances in Artificial Intelligence and Applications

4. Communication Papers

Identyfikatory

DOI

10.15439/2021F129