Prasad, AbhinavArunachalam, PrakashMotamedi, AliBhattacharya, RanjeetaLiu, BeibeiMcCormick, HaysXu, ShengzheMuralidhar, NikhilRamakrishnan, Naren2023-12-042023-12-042023-11-27https://hdl.handle.net/10919/116727This paper presents an integrated methodology to forecast the direction and magnitude of movements of lending rates in security markets. We develop a sequence-to-sequence (seq2seq) modeling framework that integrates feature engineering, motif mining, and temporal prediction in a unified manner to perform forecasting at scale in real-time or near real-time.We have deployed this approach in a large custodial setting demonstrating scalability to a large number of equities as well as newly introduced IPO-based securities in highly volatile environments.application/pdfenCreative Commons Attribution 4.0 InternationalArticle - Refereed2023-12-01The author(s)https://doi.org/10.1145/3604237.3626877