I have a daily time series that I am having issues forecasting accurately.The time series is stationary and it looks I have tried ARIMA(3,1,1),(0,1,1)- 7 Period, auto.arima(D=1), Holt-Winters, nnetar, tbats, hybridModel and accuracy(RMSE) of test dataset is about 4 which is not good at all. Seasonality in time series looks like and I can see in acf and pacf plots that there is weekly seasonality. Is there any other method/technique I could use to forecast this daily time series more accurately? I can send the data if needed.
Your series is not stationary - by definition a seasonal series is not stationary. If it were stationary then the d order in your ARIMA model would be 0.
For complex seasonalities, your best option is TBATs. You can also try Facebook Prophet if your time series is daily or above (based on your plot I assume that it is).
I took your 1461 daily values http://www.autobox.com/dave/moroni.csv representing visits to a particular lab/clinic for the 4 year period 2014-2017.
and predicted the next 31 days. . The Actual/Fit and Forecast is here ... with a less busy picture here showing actual and forecasts without limits . The residuals from the model suggest randomness ..
here are the stats for the model ..