Principle:Sktime Pytorch forecasting Model Training
| Knowledge Sources | |
|---|---|
| Domains | Deep_Learning, Training, Optimization |
| Last Updated | 2026-02-08 07:00 GMT |
Overview
Technique for executing the supervised training loop that optimizes model parameters by minimizing a loss function over training data while monitoring validation performance.
Description
Model Training executes the core optimization loop: iterating over mini-batches, computing forward passes, calculating loss, backpropagating gradients, and updating parameters. In pytorch-forecasting, Trainer.fit() orchestrates this process, delegating to the model's training_step (inherited from BaseModel) which handles loss computation, logging, and metric tracking. The training loop also executes validation at specified intervals, runs callbacks (EarlyStopping, checkpointing, LR monitoring), and manages hardware acceleration. For forecasting models, the loss function varies by model type: QuantileLoss for TFT, DistributionLoss for DeepAR, MASE for N-BEATS.
Usage
Use this principle as the final step before inference in every forecasting workflow. Trainer.fit() is called with the configured model and both training and validation DataLoaders. The Trainer's callbacks control when training stops (via EarlyStopping) and which checkpoints are saved.
Theoretical Basis
Stochastic Gradient Descent with backpropagation:
Where is the learning rate and is the mini-batch at step .
Training loop with validation:
# Abstract training loop
for epoch in range(max_epochs):
model.train()
for batch in train_dataloader:
loss = model.training_step(batch)
loss.backward()
clip_gradients(model, max_norm=gradient_clip_val)
optimizer.step()
optimizer.zero_grad()
model.eval()
val_loss = average([model.validation_step(b) for b in val_dataloader])
callbacks.on_validation_end(val_loss) # EarlyStopping checks here
Key aspects for forecasting:
- Gradient clipping prevents exploding gradients in attention/RNN models
- ReduceLROnPlateau decreases LR when validation loss plateaus
- EarlyStopping prevents overfitting by monitoring validation loss