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I was told to start with XGBoost. Is Prophet better?
Xgboost is a classifier for tabular data, prophet is for time series prediction. They are different use cases, though you can likely massage xgboost to do time series prediction if you really wanted to. So the question of which is better is "it depends"
https://tsfresh.readthedocs.io/en/latest/
https://www.sktime.org/en/v0.8.2/api_reference/auto_generate...
I was told to start with XGBoost. Is Prophet better?
Xgboost is a classifier for tabular data, prophet is for time series prediction. They are different use cases, though you can likely massage xgboost to do time series prediction if you really wanted to. So the question of which is better is "it depends"
Earlier quoted context omitted.
Xgboost is a classifier for tabular data, prophet is for time series prediction. They are different use cases, though you can likely massage xgboost to do time series prediction if you really wanted to. So the question of which is better is "it depends"
I'm not even sure how you can get it to work on time series...
Forecast package in R is quite useful. Even if you don’t use R, this book by Rob Hyndman is very approachable and easy to follow. https://otexts.com/fpp2/
Also, there's a new version; s/2/3 :)
As an example, imagine you want to calculate only a single sample into the future. Say furthermore that you have six input timeseries sampled hourly, and you don't expect meaningful correlation beyond 48h old samples.
You create 6x48 input features, take the single target value that you want to predict as output, and feed this into your run of the mill gradient boosted tree.
The above gives you a less complex approach than reaching for bespoke time-series stuff; I've personally have had success doing something like this.
If your regressor does not support multiple outputs, you can always wrap it in sklearns MultiOutputRegressor (or optionally RegressorChain; check it out). This is useful if, in the above example, you are not looking to predict only the next sample, but maybe the next 12 samples.
https://scikit-learn.org/stable/modules/generated/sklearn.mu...