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by puppy_nap
1047 days ago
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The M-* series of competitions are the current major forecasting competitions in the time series world. https://en.wikipedia.org/wiki/Makridakis_Competitions I believe the top performing models in recent years have all been gradient boosted trees, usually then fed into some secondary ensembling scheme. |
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Gradient Boosted Trees: This is a machine learning technique used for predictive modeling. It involves creating a series of decision trees, where each subsequent tree corrects the errors made by the previous ones. Think of it like a team of experts working together, where each expert specializes in fixing the mistakes made by the previous experts. This iterative process leads to a strong predictive model.
Ensembling: Ensembling is a strategy in which multiple individual models are combined to create a more robust and accurate prediction. It's like taking the opinions of multiple experts to make a better decision. In the context of gradient boosted trees, ensembling involves combining the predictions from multiple trees to get a more accurate overall prediction.
Secondary Ensembling Scheme: This refers to an additional layer of combining models to further improve accuracy. It's like having a group of experts (the gradient boosted trees) collaborate and then having another group of experts (the secondary ensembling scheme) collaborate based on the first group's insights.
So, "gradient boosted trees fed into some secondary ensembling scheme" means using a combination of decision trees that correct each other's errors, and then taking the outputs of these trees and further improving their predictions through another layer of combining models. This approach often leads to highly accurate predictions in forecasting tasks.