# Sketching the order of events

arXiv: https://arxiv.org/abs/1708.09708 · HTML: https://mlsanity.com/paper/1708.09708/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2017.

## Built on by (most cited first, 2 of 2)

- [Random Fourier Signature Features](https://mlsanity.com/paper/2311.12214/) (2023)
- [Learning to Forget: Bayesian Time Series Forecasting using Recurrent Sparse Spectrum Signature Gaussian Processes](https://mlsanity.com/paper/2412.19727/) (2024)

## Abstract (arXiv)

We introduce features for massive data streams. These stream features can be thought of as "ordered moments" and generalize stream sketches from "moments of order one" to "ordered moments of arbitrary order". In analogy to classic moments, they have theoretical guarantees such as universality that are important for learning algorithms.