mlforecast VS LazyProphet

MLForecast

Alternatively you could try out their mlforecast package which 'featurizes' the time pieces to fit with things like LightGBM: https://github.com/Nixtla/mlforecast

- https://nixtla.github.io/mlforecast/

Leaving these two repos here for anyone interested in trying decision tree regression or statistical forecasting baselines: - https://nixtla.github.io/mlforecast/ - https://github.com/Nixtla/statsforecast

In Python you can use the https://nixtla.github.io/mlforecast library for example, it makes the feature engineering, evaluation and cross validation trivial.

You might want to take a look to this library: MLForecast.

Yes, for example we have this paper in long-horizon settings using our library NeuralForecast and this experiment with other of our libraries MLForecast, both of them outperforming autoarima.

We are already working on the comparison. For the moment, the blog shows that another of our libraries, MLForecast (https://github.com/Nixtla/mlforecast), has an excellent performance in this use case.

We missed that, sorry. At the moment, for forecasting the pipeline uses the mlforecast library (https://github.com/nixtla/mlforecast) that builds upong Sckilearn .xgboos and lightbmg .

If you want a quick thing to try for single time series you can try my package: LazyProphet which uses LightGBM under the hood.

If you are feeling adventurous you could try some of my packages: ThymeBoost or LazyProphet. ThymeBoost is interesting as it is gradient boosting around time series decomposition. So you will still have the trend/seasonality decomposition but with more exotic methods. LazyProphet is just some feature engineering for time series fed into Lightgbm but it tends to perform well enough. Both tend to outperform fbprophet although that generally isn't too hard to do and they both have automatic fitting procedure that performs ok.