I have a time series and a plot of it is presented below for consideration.
A linear trend was identified in the series both visually and using statistical tests such as Cox-Stuart and ManKendall. The statistical tests showed the presence of trend in the series. The ashtonishing thing happened when I modelled using Holt-Winters' exponential smoothing without trend/Beta with multiplicative and additive seosonality. The results that came out from both models were close to being the actual values. SMAPE of 0.011 and 0.009. I checked the residuals immediately to see if something has gone wrong but to my amusement there is one significant bar amongst the first 20 lags which can be due to chance. Now, what I am trying to figure out is the logic behind this phenomenon. I am new to the field of forecasting and If I do not make sense then please bear with me.
The ACF of exponential model without trend and additive seasonality is given below.
I have also tried other holt-winters' exponential models with all 3 parameters included but the residuals showed high autocorrelation and the SMAPE's were significantly low but not as low as the the SMAPE of the model mentioned above.
I would like an expert opinion on this please.