Impute values for missing holiday values (or any values) by taking an average of the day before and the day after. This is rudimentary just to get going and can ultimately be modified/corrected via Intervention Detection procedures. Set your seasonality to 5 and jointly identify arima and latent deterministic effects like daily effects , pulses , step-level shifts , seasonal pulses i.e. changes in daily effects over time and local time trends following http://docplayer.net/12080848-Outliers-level-shifts-and-variance-changes-in-time-series.html and an iterative modelling process similar to https://autobox.com/pdfs/ARIMA%20FLOW%20CHART.pdf . Follow this thread for a practical example Simple method of forecasting number of guests given current and historical data .
You also might load up 11 monthly indicators to potentially capture monthly effects (and/or weekly dummies to capture possible weekly effects ) as you form your SARMAX model as here suggested here https://autobox.com/pdfs/SARMAX.pdf