Time Series Forecasting with Data Transform and Its Application in Sport

Forecasting time series data is an exciting challenge. Although being complex, this is a high potential for industrial use. One of the most significant gaps in the forecasting process is the quality of data representation, especially with the time-series data. This paper proposes an effective method using an integral transform that can show hidden information of the time series data. The integral transform exploits data as a composition of many basic functions and then use this set to present the data. Mathematically, this transform converts the data into another space with another feature, showing many properties hidden in the original form. The experimental result demonstrates our suggestion can learn the transformation rules and then can be applied for many applications.

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Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).