I've been working for months on short-term load forecasting and the use of climate/weather data to improve the accuracy. I have a computer science background and for this reason I'm trying to not make big mistakes and unfair comparisons working with statistics tools like ARIMA models. I'd like to know your opinion about a couple of things:
I'm using both (S)ARIMA and (S)ARIMAX models to investigate the effect of weather data on forecasting, do you think it would be necessary to use also Exponential Smoothing methods?
Having a time series of 300 daily samples I'm starting from the first two weeks and I perform a 5 days-ahead forecast using models built with auto.arima R function (forecast package). Then, I add another sample to my data set and I calibrate again the models and I perform another 5 days forecast and so on until the end of the available data. Do you think this way to operate is correct?
Thank you for your suggestions, although the target of our work is an engineering journal article, I would like to do a work as rigorous as possible from a statistical point of view.