Robust estimation of SARIMA models: Application to short-term load forecasting

This paper presents a new robust method to estimate the parameters of a SARIMA model. This method uses robust autocorrelations estimates based on sample medians coupled with a robust filter cleaner which rejects deviant observations. Our procedure is compared with other robust methods via evaluation of the different robustness measures such as maximum bias, breakdown point and influence function. The asymptotic properties of our method (strong consistency and central limit theorem) are established for a gaussian AR process. We show that the method improves the French load forecasting for "normal days" and offers good robustness, easiness and fast execution.