What is ensemble learning in time series forecasting?

Ensemble learning in time series combines multiple forecasting models to improve prediction stability and accuracy.

Time series ensembles combine statistical models (ARIMA), machine learning models (XGBoost), and deep learning models (LSTM). Each model captures different temporal patterns such as trend, seasonality, and nonlinear dependencies. Combining them reduces forecasting error and improves robustness to noise and structural breaks.