Ensemble Model
A model that combines predictions from several independent models, typically producing more robust forecasts than any single model alone.
An ensemble aggregates the outputs of multiple base models. Because different architectures make different kinds of errors, averaging or stacking their predictions cancels out idiosyncratic mistakes and tends to improve accuracy and stability — the same intuition behind a panel of forecasters outperforming any individual.
endeavr.ai runs a four-model neural ensemble — an LSTM with attention, a GRU with skip connections, a Temporal CNN in the WaveNet style, and a Transformer encoder — combined through a stacking meta-learner. Each base model sees the same 45 engineered features but captures different temporal structure.