Kaggle time series

XGBOOST – Why is it so Important? In broad terms, it’s the efficiency, accuracy, and feasibility of this algorithm . It has both linear model solver and tree learning algorithms. So, what makes it fast is its capacity to do parallel computation on a single machine.

As XGBoost is very good at identifying patterns in data, if you have enough temporal features describing your dataset, it will provide very decent predictions .

XGBoost is faster than the LSTM method with equal precision in the correct tuning parameters . The drawback is its feature-importance is not so accuracy as LSTM+SHAP combination.

When to NOT use XGBoost

XGBoost is still a great choice for a wide variety of real-world machine learning problems . Neural networks, especially recurrent neural networks with LSTMs are generally better for time-series forecasting tasks. There is “no free lunch” in machine learning and every algorithm has its own advantages and disadvantages.

There are multiple multivariate forecasting methods available like — Pmdarima, VAR, XGBoost etc. In this blog, we’ll focus on the XGBoost (Extreme Gradient Boosting) regression method only . First we’ll use AR (AutoRegressive) model to forecast individual independent external drivers.3 Şub 2022