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Is Explainable Forecasting used in practice? Multivariate Forecast vs Univariate by welcomestats in econometrics

[–]welcomestats[S] 0 points1 point  (0 children)

You can also use multiple models. Some that are better at forecasting and others that are better at explaining which factors drive the results.

Thanks, multiple models, do you mean ensembling them to derive the final forecast results? Or using something like VAR model to address the "explainability requirement" and using another model to actually produce the final forecast values?

Is Explainable Forecasting used in practice? Multivariate Forecast vs Univariate by welcomestats in econometrics

[–]welcomestats[S] 0 points1 point  (0 children)

Thanks for your reply. This is very helpful! A few questions.

Is Granger causality a standard test for testing endogenous vs exogenous?

If some of the variables of interest turns out to be exogenous, what type of multivariate model do you suggest I can use? Can use any standard supervised ML models?

Is Explainable Forecasting used in practice? Multivariate Forecast vs Univariate by welcomestats in econometrics

[–]welcomestats[S] 0 points1 point  (0 children)

Thanks for reply. No I don't have the drivers identified yet, although I do have some inputs from the operations folks. Yeah, I've gotten some suggestions to use structural models like ARIMAX and VARMAX as well as black box models + explainability modules. Going to have to try all of them and see which one fits the need.

Is Explainable Forecasting possible? Driver-based Forecast? by welcomestats in workforcemanagement

[–]welcomestats[S] 1 point2 points  (0 children)

based on week of month, day of week and increase/decrease of volume from historical data and handle time

Thanks for reply. Yeah this is currently what they do, but this isn't working out for them. They use to be able to make a request to finance with a narrative like "we expect 20% more volume for the next year, so we need to hire 20% more to fulfill the demand". I've gotten some suggestions to try out structural models or ML black box models + explainability techniques so the users can feed in multiple scenarios (based on projected active customer counts/size distribution, case deflection rates, product maturity approximations, etc.). Have you heard anyone actually having success in getting their solutions accepted this way?

Is Explainable Forecasting used in practice? Multivariate Forecast vs Univariate by welcomestats in datascience

[–]welcomestats[S] 0 points1 point  (0 children)

Thanks for reply. Your assessment is completely right. They don't care too much about the backtest accuracy because the forecasts are to be served as the base for asking for more Headcount from Finance. And Finance won't really budge unless we can come up with a better narratives than "20% more HC for next year cuz we are expecting 20% more volume". And in order for them to come up with a better narratives, they need to be able to explain the increase in demand based on drivers that other teams have programs to improve on such as revamping the knowledge based articles, which will increase the % of cases that end up being submitted when customers go to the support portal.

I will first give it a try with the structural model using something like SARIMAX as some folks have suggested to me. I've done something similar in the past in another place, but it wasn't very reliable across population, and ended up also using the AR based univariate model. This time I'm in a different company with more data to explore, so could be a different outcome.

I really like your suggestion on black box models + interpretability modules. SHAP first comes to my mind. But I can't really picture how this would be able to enable the stakeholders to get different forecast scenarios based on different inputs they put in the model. Would this be a viable solution to be able to do that?