# Beta seasonal autoregressive moving average models

arXiv: https://arxiv.org/abs/1806.07921 · HTML: https://mlsanity.com/paper/1806.07921/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2018.

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

- [Aligning Visual Contrastive learning models via Preference Optimization](https://mlsanity.com/paper/2411.08923/) (2024)
- [Prediction Intervals in the Beta Autoregressive Moving Average Model](https://mlsanity.com/paper/2207.11628/) (2022)

## Abstract (arXiv)

In this paper we introduce the class of beta seasonal autoregressive moving average (βSARMA) models for modeling and forecasting time series data that assume values in the standard unit interval. It generalizes the class of beta autoregressive moving average models [Rocha and Cribari-Neto, Test, 2009] by incorporating seasonal dynamics to the model dynamic structure. Besides introducing the new class of models, we develop parameter estimation, hypothesis testing inference, and diagnostic analysis tools. We also discuss out-of-sample forecasting. In particular, we provide closed-form expressions for the conditional score vector and for the conditional Fisher information matrix. We also evaluate the finite sample performances of conditional maximum likelihood estimators and white noise tests using Monte Carlo simulations. An empirical application is presented and discussed.