Univariate time series: Only one variable is varying over time . For example, data collected from a sensor measuring the temperature of a room every second. Therefore, each second, you will only have a one-dimensional value, which is the temperature. Multivariate time series: Multiple variables are varying over time.
Read moreWhat is multivariate multi step time series forecasting?
What is Multivariate Forecasting ? If the model predicts dependent variable (y) based on one independent variable (x), it is called univariate forecasting. For Multivariate forecasting, it simply means predicting dependent variable (y) based on more than one independent variable (x) .
Read moreWhy is XGBoost so powerful?
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.
Read moreIs XGBoost Good for forecasting?
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 .
Read moreWhy is XGBoost better than Lstm?
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.
Read moreWhen should I not use XGBoost?
When to NOT use XGBoost
Read moreIs XGBoost still good?
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.
Read more