cenn-forecasting 0.1.0

Cellular Neural Network (CeNN) for multivariate time-series forecasting

CeNN Forecasting

A PyTorch implementation of Cellular Neural Networks (CeNN) adapted for multivariate time-series forecasting.

Overview

This package provides a trainable CeNN model based on the theory of Chua & Yang (1988), adapted for temporal forecasting tasks. Key features:

• Forward Euler discretization of the CeNN state equation with learnable step size
• Causal masking to prevent information leakage from future time steps
• Depthwise or cross-channel convolution modes for univariate/multivariate coupling
• Pre-norm residual blocks with LayerNorm for stable deep stacking
• Compatible with NeuralForecast via the CeNN wrapper class

Installation

pip install cenn-forecasting

Quick Start

Standalone usage

import torch
from cenn_forecasting import CeNNModel1D

model = CeNNModel1D(
    n_features=7,       # number of time series
    seq_length=96,      # input window length
    pred_length=24,     # forecast horizon
    hidden_dim=64,      # latent dimension
    N=2,                # number of CeNN blocks
    K=8,                # recurrent steps per layer
    dropout=0.1,
    num_layers=2,
    neighborhood=3,     # convolution kernel size (odd)
    alpha_init=0.9,     # initial integration parameter
)

x = torch.randn(32, 96, 7)  # [batch, time, features]
out = model(x)               # [32, 24, 7]

With NeuralForecast

from neuralforecast import NeuralForecast
from neuralforecast.models import CeNN
from neuralforecast.losses.pytorch import MSE

model = CeNN(
    h=96,
    input_size=512,
    n_series=7,
    hidden_dim=64,
    N=2, K=8,
    num_layers=2,
    loss=MSE(),
    max_steps=1000,
    scaler_type='robust',
    accelerator='gpu',
)

nf = NeuralForecast(models=[model], freq='h')
nf.fit(df=train_df, val_size=val_size)
forecasts = nf.predict()

Architecture

Input [B, L, C]
    │
    ├── Linear projection → [B, L, hidden_dim]
    ├── Permute → [B, hidden_dim, L]
    │
    ├── CeNN Stack (N blocks)
    │     └── CeNN Block
    │           ├── ResidualNorm + CeNNLayer (×num_layers)
    │           │     └── CeNNCell (K iterations)
    │           │           ├── v' = α·v + (1-α)·(A·tanh(v) + B·u + I)
    │           │           └── y = tanh(v')
    │           └── Final LayerNorm
    │
    ├── Linear temporal projection → [B, hidden_dim, H]
    ├── Permute → [B, H, hidden_dim]
    └── MLP prediction head → [B, H, C]

References

• L.O. Chua and L. Yang, "Cellular Neural Networks: Theory," IEEE Trans. Circuits and Systems, 1988.
• L.O. Chua and L. Yang, "Cellular Neural Networks: Applications," IEEE Trans. Circuits and Systems, 1988.

License

Apache 2.0