# Rollage: Efficient Rolling Average Algorithm to Estimate ARMA Models for Big Time Series Data

arXiv: https://arxiv.org/abs/2103.09175 · HTML: https://mlsanity.com/paper/2103.09175/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2021.

## Built on by (most cited first, 3 of 3)

- [Toeplitz Least Squares Problems, Fast Algorithms and Big Data](https://mlsanity.com/paper/2112.12994/) (2021)
- [SALSA: Sequential Approximate Leverage-Score Algorithm with Application in Analyzing Big Time Series Data](https://mlsanity.com/paper/2401.00122/) (2023)
- [A Hybrid Statistical-Machine Learning Approach for Analysing Online Customer Behavior: An Empirical Study](https://mlsanity.com/paper/2212.02255/) (2022)

## Abstract (arXiv)

We develop a new efficient algorithm for the analysis of large-scale time series data. We firstly define rolling averages, derive their analytical properties, and establish their asymptotic distribution. These theoretical results are subsequently exploited to develop an efficient algorithm, called Rollage, for fitting an appropriate AR model to big time series data. When used in conjunction with the Durbin's algorithm, we show that the Rollage algorithm can be used as a criterion to optimally fit ARMA models to big time series data. Empirical experiments on large-scale synthetic time series data support the theoretical results and reveal the efficacy of this new approach, especially when compared to existing methodology.