  The task of generating rich and fluent narratives that aptly describe the
characteristics, trends, and anomalies of time-series data is invaluable to the
sciences (geology, meteorology, epidemiology) or finance (trades, stocks, or
sales and inventory). The efforts for time-series narration hitherto are
domain-specific and use predefined templates that offer consistency but lead to
mechanical narratives. We present TCube (Time-series-to-text), a
domain-agnostic neural framework for time-series narration, that couples the
representation of essential time-series elements in the form of a dense
knowledge graph and the translation of said knowledge graph into rich and
fluent narratives through the transfer-learning capabilities of PLMs
(Pre-trained Language Models). TCube's design primarily addresses the challenge
that lies in building a neural framework in the complete paucity of annotated
training data for time-series. The design incorporates knowledge graphs as an
intermediary for the representation of essential time-series elements which can
be linearized for textual translation. To the best of our knowledge, TCube is
the first investigation of the use of neural strategies for time-series
narration. Through extensive evaluations, we show that TCube can improve the
lexical diversity of the generated narratives by up to 65.38% while still
maintaining grammatical integrity. The practicality and deployability of TCube
is further validated through an expert review (n=21) where 76.2% of
participating experts wary of auto-generated narratives favored TCube as a
deployable system for time-series narration due to its richer narratives. Our
code-base, models, and datasets, with detailed instructions for reproducibility
is publicly hosted at https://github.com/Mandar-Sharma/TCube.
