Deep Learning✓ Mathematical
◆ The PatternPure MLP architectures for forecasting
N-BEATS uses stacks of fully-connected blocks that produce both a backward forecast (reconstructing the input) and a forward forecast. Residual connections between blocks let each stack focus on what previous stacks missed. The interpretable variant decomposes into trend and seasonal basis functions. N-HiTS adds hierarchical interpolation for efficiency.
// Interactive — N-BEATS block architecture
# Python — N-BEATS with Darts from darts.models import NBEATSModel model = NBEATSModel( input_chunk_length=96, output_chunk_length=24, generic_architecture=True, num_stacks=30, num_layers=4 ) model.fit(train_series) pred = model.predict(n=24)
Pattern bridge: N-BEATS’ residual stacking works like boosting — each block fits the residual from the previous one. The backward/forward forecast split mirrors the train/validation concept built into the architecture itself.