Pardon me, I am new to timeseries forecasting. Given that there is not always a clear cut way to know whether your forecasting model is good enough and there's a significant degree of subjectivity in measuring this or even defining what "good enough" means, I thought it would be interesting and educative to find out what people do in practice.
What are the modelling / quantitative criteria that you use to determine that you have a good enough timeseries forecasting model in practice?
I define a model that's good enough as one that produces reasonable enough forecasts of a timeseries in practise. Perhaps the question should be: what are the modelling/quantitative criteria that you use to determine that you have a model whose forecasts you believe to be reasonable? Are there certain things you would not accept for your forecasting model (e.g. correlated residuals) - what are they and why?
(You may assume that you have a good idea of what the regressors are and you have the future values for them)