John and others ..... Here's a link to the original M5 ( Makradakis) competition submissions and outcome

"I've been working in Python for about 8 years. I've developed some XGBoost models on time series data that are better than a coin toss, which doesn't necessarily make them actionable. I tried RNNs on the same data, thinking they would do better with the time series. But the so far the XGBoost models have performed better. That seems to be consistent with this persons experience:"

https://www.sciencedirect.com/science/article/pii/...

The winning methods were all practically based on LGBMs .... this is a version of a booster developed at Revolution R ( now part of Microsoft) .... it has some distinct advantages - especially computationally ....but its not a 1-to-1 XGB beater. Infact the latest versions of XGB has tried to incorporate some of the advances from LGBM into it. I think the lintree ( Linear tree) especially is well suited for Time Series problems. I practically almost always use LGBM if using boosting ( with XGB as a benchmarker).


Hope this helps!

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