There is a quiet frustration embedded in the question of how we debug a bad forecasting model. It is not the frustration of a missing algorithm or a cleverer loss function. It is the frustration of realizing that most of our diagnostic energy goes into comparing one overall error score against another, while the real story lives in the messy, segmented, horizon-dependent details. The question being asked sounds simple but is profoundly difficult: when the forecast is worse than the business wants, where do you actually start? Not with a checklist, but with a workflow. And that distinction matters, because a workflow is something you can build on, while a checklist is just a wall you hit your head against.
We have seen this pattern before in our own coverage of evaluation practices. In Beyond MSE: Refining Forecasts with Autoregressive Rollout and Uncertainty, the argument was that point estimates hide too much. The same logic applies here. A single error metric, even a carefully chosen one, is a summary, not a diagnosis. The instinct to break errors down by customer, product, location, or horizon is exactly right. But what is notable is the second half of the question: what did you have to build yourself? That is where the real pain lives. The tools that exist today, whether they are commercial platforms or open source libraries, give you plots and tables. They rarely give you a path. So the practitioner ends up writing custom notebooks, reshaping dataframes, and manually aligning timestamps just to see if the error is worse at hour 24 than at hour 6. That is not a technical gap. It is a design gap.
The update clarifying that they are not looking for an if-else explanation is the most important part of this post. It signals a mature understanding that forecasting diagnostics are contextual. The answer depends on the data, the objective, and the decision the model supports. What they are really exploring is whether there is room for a small open-source tool that standardizes the *process* of evaluation without pretending to know the answer in advance. That is a different ambition from building another auto-ML wrapper. It is closer to what Explore the Forrester Function: Beyond Mathematics, a Tool for Machine Learning hinted at: that sometimes the most useful tools are not the ones that make decisions for you, but the ones that make the decision surface visible. Similarly, Cloudflare's Blog Finds Performance Gains with EmDash, Its New CMS is a reminder that open source solutions often win because they reduce friction, not because they add features.
What we would tell the author, and anyone else who has stared at a bad forecast with a sinking feeling, is this: the problem is not that you lack a method. It is that you lack a shared vocabulary for what you are looking at. The most valuable contribution a small tool could make is not another chart, but a structured way to ask the next question. Did the model fail because the data changed, because the validation setup leaked information, or because the business expectation was never aligned with the metric in the first place? That last one matters more than most people admit. A tool that forces you to state your metric, your baseline, and your segmentation before you look at a single residual would do more for your productivity than any new model architecture.
The open question, and the one worth watching, is whether the community is ready for that kind of discipline. We suspect the community is ready, but only if the tool is built with the same humility that is being shown. Not as a solution, but as a mirror. We would tell them: do not build a dashboard. Build a question. And then make the question answerable in under ten minutes. That is the only way this gets adopted.