I see. The link seems helpful so i will most probably try it.
I am planing to work with time series datasets so the question is:
Does thingworx prescriptive thing work with time series datasets?
I was reading up on how prescriptive analytics can in real time suggesting actions to benefit from the predictions and show the implications of each decision option on the end goal eg. profit so i wonder if thingworx has such functionality.
Interestingly, i realize that you can get the the following error for wrong range input. How does the TWA determine the correct boundary for this dataset?
Failed to score: Invalid range specified for the continuous field [Cement].
Acceptable bound(s): [102.0,540.0]
is it possible to briefly explain how the prescriptive analytics finds the values to maximise or minimise the goal? I got a little confused as the model i trained on is a mashup of neural network and some other algorithm. From what i understand, it acts like a black box with a lot of functions in the background so it seem to be impossible to find the global max or min.
Does Thingworx(prescriptive analytics) use brute force on all the possible values of the chosen levers to find the most optimal value?
During pre-sales I often get asked by customers about the details on how TWA works under-the-hood. Is it documented somewhere, besides the info in help center?
I mean, I can explain DS concepts to people, but the people asking such questions already have some DS expertise and they want to know what approaches are implemented in TW Analytics to try to estimate quality of the models and predictions they'll be getting, otherwise they're reluctant to trust the "black box" software.
Even if we persuate the decision maker and win the deal, having a sceptical DS guy who will be implementing the solution will not be benefical to the project.
Yes, predictions themselves is just one piece of TW and TWA functionality and the product has other major benefits which I communicate during pre-sales, but I couldn't find a good way to handle such kind of questions.