Abstract

Rabindranath Tagore (1861-1941) was a world famous Nobel laureate poet, philosopher, educationist and one of the most prolific song writers and composers in India’s history. He was the first Nobel laureate of Asian origin who received the Nobel prize in literature in 1913. Among his numerous manifestations of innovation, creation and excellence – his songs were very special to him. He wrote and composed songs since he was only eleven years and continued till the end of his day. Among the many facets of his creative works, the composition of his about 2200 songs stand out in sheer number, artistic caliber, scholarly appreciation and social impact. In this paper we focus on his productivity of songs by year, in thematic as well as non-thematic categorizations. We may visualize the total number of annual song-counts as a time series X(t) where t denotes age or year. We summarize and graphically study this time-series as well as nonparametrically study its changepoints. We observe the upper and lower 5% quantile counts and associate the years that relate to extremely ‘low’ or ‘high’ productivity. We also associate the changepoints with major events (turning points) of his life. We also view the multivariate time series by the themes or categories – namely ‘Puja’ (Devotion), ‘Prem’ (Love), ‘Prakriti’ (Nature), ‘Swadesh’ (Motherland) and ‘Vichitra’ (Medley). Thematic categorizations then give rise to a multivariate time series We summarize and graphically describe the time-series, focusing on the upper and lower extremes of these counts, as well as the corresponding central tendencies, skewness, shapes, temporal patterns and variations as well as changepoints. We observe that the high extremal counts are always associated with a handful of distinct, extreme and unexpected events in Tagore’s life. We further study changepoints and observe that these are associated with either a handful of distinct, extreme events or some turning-points in Tagore’s life. To our knowledge Tagore’s song-counts have never before been studied as a time-series and hence the insight we find here is novel to both statisticians as well as Tagore-enthusiasts.

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