Clustering currency exchange rates data using time series clustering technique based on autocorrelation-based fuzzy c-means similarity measure.

Exchange rate instability poses a global economic challenge that impacts international trade and the overall economies of countries, affecting their human development as well. This paper proposes a method that clusters the exchange rate time series of countries and it's compared with the Human Development Index (HDI) categorization. By doing so, a deeper understanding of the relationship between currencies and development status can be gained. In this study, a fuzzy clustering framework was employed to group the time series of exchange rates for specific countries. A total of twenty-five currencies were chosen for analysis during the period from January 01, 2014, to December 31, 2018. Time series clustering was conducted based on the autocorrelation patterns of the time series. The fuzzy c-means approach was used to calculate the similarity between their autocorrelations. The analysis findings indicate the Autocorrelation-FCM approach demonstrated better cluster results compared with the FCM. Moreover, it was observed that the clustering of exchange rates significantly influenced the clustering of HDI. Based on these results, it is recommended that policymakers adopt a diverse range of policies to ensure stability in the exchange rate to foster human development. Such measures hold the potential to bring about significant changes and improvements in monetary policy across different nations. [ABSTRACT FROM AUTHOR]