Prasad Shinde

The biggest IT story in the last decade is how falling compute costs have enabled new modeling techniques that were previously impractical, such as machine learning and deep neural networks. But what is the state of ML and AI (even transformers?) for time series, and where are things heading?

Getting large language models (LLMs) to provide accurate and relevant output is a well-known challenge. It’s even more daunting in the fast-moving landscape of capital markets.

It's no secret that the automation of insights drives forward the financial industry. Both traditional quantitative analysis and newer AI models require a solid data foundation.