Proceedings of the 2021 Sixth International Conference on Research in Intelligent and Computing

DOI: http://dx.doi.org/10.15439/2021R06

Citation: Proceedings of the 2021 Sixth International Conference on Research in Intelligent and Computing, Vijender Kumar Solanki, Nguyen Ho Quang (eds). ACSIS, Vol. 27, pages 29–32 (2021)

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Abstract. 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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