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Tensor format

All IceNet-MP models operate on tensors in NTCHW format:

Dimension Meaning
N Batch size
T History steps (inputs) or forecast steps (outputs)
C Channels / variables
H Height
W Width

N and T are the same across all inputs, but C, H, and W may differ per dataset.

For example, with 3 history steps, and 4 forecast steps, each of the k inputs each have shape (N, 3, C_k, H_k, W_k) and the output has shape (N, 4, C_out, H_out, W_out).

Standalone models

A standalone model accepts a dict[str, TensorNTCHW] mapping dataset names to tensors and produces output of shape (N, T, C_out, H_out, W_out). Each model instance is typically trained to predict a single output, although this is not a hard constraint.

Standalone pipeline diagram
Pros All input variables available without transformation.
Cons Combining datasets of different shapes or types must be done inside the model.

Processor models

A processor model sits inside an encode-process-decode pipeline. You define a latent space (H_latent, W_latent) and the framework automatically creates one encoder per input and one decoder per output.

  1. Each dataset-specific encoder maps input (N, T_history, C_k, H_k, W_k) to (N, T_history, C_k_latent, H_latent, W_latent).
  2. The k encoded tensors are concatenated to (N, T_history, C_latent, H_latent, W_latent).
  3. The processor maps (N, T_history, C_latent, H_latent, W_latent) to (N, T_forecast, C_latent, H_latent, W_latent).
  4. Each output-specific decoder maps the processor output, (N, T_forecast, C_latent, H_latent, W_latent), to (N, T_forecast, C_out, H_out, W_out).

Encode-process-decode pipeline diagram
Pros Inputs are converted into a common latent space, freeing up the model to learn time evolution.
Cons Latent space representation may lose some spatial correlations present in the inputs.