Bayesian inference for double SARMA models

In this paper, we present a Bayesian analysis of double seasonal autoregressive moving average models. We first consider the problem of estimating unknown lagged errors in the moving average part using non linear least squares method, and then using natural conjugate and Jeffreys’ priors we approximate the marginal posterior distributions to be multivariate t and gamma distributions for the model coefficients and precision, respectively. We evaluate the proposed Bayesian methodology using simulation study, and apply to real-world hourly electricity load data sets.