In this paper, we will look at the compatibility of different forecasting methods applied to time series data in esports, specifically three esports, League of Legends, Counter Strike:Global Offensive and Defence of the Ancients 2. The purpose of the study is to assess whether forecasting the amount of professional esport matches for the first three months of 2021 is possible and if so, how accurately. The forecasting methods used in the report are seasonal ARIMA (SARIMA), autoregressive neural networks (NNAR) and a seasonal naïve model as a benchmark. The results show that, for the chosen methods, all the three datasets were able to fulfill the statistical requirements for producing forecasts as well as outperforming the benchmark model, although with various results. Considering the three games, the one that the study was able to predict with highest accuracy was the CS:GO dataset with a NNAR model where we achieved a mean absolute percentage error of 31%.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-445876 |
| Date | January 2021 |
| Creators | Englesson, Christopher, Karlin, Ludvig |
| Publisher | Uppsala universitet, Statistiska institutionen, Uppsala universitet, Statistiska institutionen |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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