Empirical: A language for time-series analysis
Empirical is a programming language designed specifically for time-series analysis, aiming to simplify the process of working with sequential and temporal data.
Background
- Empirical is a new, domain-specific programming language designed specifically for working with time-series data (stock prices, sensor readings, server metrics, etc.), rather than using general-purpose languages like Python or R.
- It aims to simplify common time-series tasks — reshaping, aligning, rolling calculations, joining on timestamps — that are awkward and error-prone in existing tools.
- The project was created by a small team with experience at Palantir and Jane Street, two firms known for serious data engineering and quantitative finance.
- Time-series analysis is central to fields like algorithmic trading, IoT monitoring, scientific research, and MLOps, but most data tooling (SQL, pandas, Spark) treats timestamps as an afterthought rather than a first-class concept.
- Empirical is still in early development; the linked page is a landing site describing the vision, not a downloadable product.