As a statistician, I'm inclined to say "no, you can't" as a short answer. Reason for this is simple: even in complete random datasets on average 5% of the correlations will be significant when tested in a model. So if you rely on only the data to make any kind of interpretation on association of variables, you're bound to publish false positives. This has been discussed for decades, eg this rather strong opinion of Ioannidis (2005) :
Bias can entail manipulation in the analysis or reporting of findings.
Selective or distorted reporting is a typical form of such bias.
If you don't formulate a hypothesis and still interprete the data, you're -probably unintentionally- reporting selectively. You select from the analysis those results that tell a story, and in doing so, you're likely to report something that isn't a solid association or relation.
That said, you don't always have to formulate a specific hypothesis. For example, if you compare multiple methods on efficiency, you don't have to hypothesize beforehand which one is going to be the best. But the statistical test you use for comparison, will imply a "null hypothesis" that there is no real difference between all methods. Also this is "formulating a hypothesis" merely by the choice of analysis tools.
And this is even more important to realize: you might not formulate a hypothesis explicitly, but the nature of the statistical tools you use to come to your interpretation, will imply a set of rather rigid hypotheses and assumptions. You need to be aware of those hypotheses and also of those assumptions.
Because that's something I see far too often: people not explicitly formulating a hypothesis, still interpreting results from a statistical methodology, but failing to realize that their data does not meet the assumptions of that methodology. And that invalidates your entire interpretation.
This problem is even more stringent when forecasting. If you use regression models, you should be aware that predictions outside the boundaries of the original data cannot be interpreted. The uncertainty on those predictions is simply too big. If you use spline methods, you can even get into trouble at the edge of your original data. So definitely in the case of forecasting I would write out both the goal of the research and what you expect the predictions to show, including the scientific reason why. Only in those cases you can use forecasts as some form of evidence for or against the expected relation. If you don't do that, your forecasting model might as well be a fancy random number generator.
So in conclusion: even if your research goal isn't necessarily a defined hypothesis, you still need to formulate the hypotheses you want to test before carrying out the actual statistical tests.
And in all honesty, writing down what you expect to see is always a good idea, even if it's only to order your own thoughts.