Fuzzy time series based on frequency density-based partitioning and k-means clustering for forecasting exchange rate
| Autor: | Farikhin, Budi Irawanto, U. S. Mukminin, B. Surarso |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Journal of Physics: Conference Series. 1943:012119 |
| ISSN: | 1742-6596 1742-6588 |
| DOI: | 10.1088/1742-6596/1943/1/012119 |
| Popis: | Fuzzy Time Series (FTS) has been growing rapidly in recent years. There are many models that were developed. In this paper, we propose a new method to forecast exchange rate data by combining some models. Firstly, we use the average-based interval to make optimal interval numbers. Secondly, we use frequency density-based partitioning for optimal partitioning. In this part, we divide the three highest frequency of intervals into four, three, and two sub-intervals, respectively, and discarding intervals if there is no data distributed. And thirdly, we use k-means clustering to construct the Fuzzy Logical Relationship Group (FLRG). We divide Fuzzy Logical Relationship (FLR) into 16 initial clusters. Then we evaluate model by calculating the error value using MSE (Mean Squared Error) and AFER (Average Forecasting Error Rates). The study case of this paper is daily exchange rate data (USD to IDR) started from January until May with its unstable fluctuation caused by Pandemic Covid-19. The study aims to obtain a forecasting model of exchange rate data as the preparation and evaluation for future conditions. |
| Databáze: | OpenAIRE |
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