Timeline for Forecasting a Time Series Model for 1000s of Time Series
Current License: CC BY-SA 4.0
4 events
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| Aug 8, 2023 at 11:02 | comment | added | Stephan Kolassa |
No, I would recommend training a model on all predictors that make sense, and TeamID is definitely one of those (since you want to predict future tasks for a Team you presumably have seen in your training data). It might make sense to instead use features of your teams, like number of developers, number of architects etc. in your team. And the exact same holds for TaskID: if you want to predict for a task you have already seen in your training data, use this as a predictor. I am just wondering whether this makes sense, or whether you should rather use task features.
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| Aug 8, 2023 at 10:49 | vote | accept | Tirth | ||
| Aug 8, 2023 at 10:49 | comment | added | Tirth | Thank you, To clarify, when you suggest using standard regression models, are you recommending training a regression model directly on the entire dataset without considering the TeamID and TaskID? In other words, should I disregard TeamID and TaskID and build a regression model solely based on the predictor variables? Or do multiple time series forecasting considering Task and Team ID. | |
| Aug 7, 2023 at 10:21 | history | answered | Stephan Kolassa | CC BY-SA 4.0 |