КЛАСТЕРИЗАЦІЯ ЧАСОВИХ РЯДІВ НА ОСНОВІ НАЯВНОСТІ ТРЕНДУ ЗА ДОПОМОГОЮ МАШИННОГО НАВЧАННЯ

Abstract

The article discusses the use of machine learning methods for clustering time series according to specified criteria, allowing to determine the presence of a trend component. Several clustering methods are used including k-means . The article explores several criteria for detecting trends in short time series, which are often noisy . Experimental results indicate that using these criteria as features demonstrates high clustering accuracy, with both quantitative and qualitative metrics. The results depend on various factors, such as the length of the time series, the type of trend, and the choice of clustering method.