nn_model_selector: Neural Network Model Selector

In autoann: Neural Network–Based Model Selection and Forecasting

Neural Network Model Selector

Description

Fits multiple single-hidden-layer neural network models by evaluating all possible predictor combinations and hidden node sizes. The best model is selected based on minimum RMSE on test data.

Usage

nn_model_selector(
  data,
  response_var,
  train_ratio = 0.75,
  max_nodes = 10,
  maxit = 500,
  seed = 123
)

Arguments

data	A data frame containing the response and predictor variables.
response_var	Character string specifying the response variable name.
train_ratio	Proportion of data used for training (default = 0.75).
max_nodes	Maximum number of hidden layer nodes to evaluate (default = 10).
maxit	Maximum number of iterations for neural network training (default = 500).
seed	Random seed for reproducibility (default = 123).

Details

Predictors are standardized before model fitting. Model performance is evaluated using RMSE and MAPE.

Value

A list containing:

• best_predictors: Predictor variables of the best model

• best_hidden_nodes: Optimal number of hidden nodes

• best_performance: RMSE and MAPE of the best model

• performance_table: Performance metrics for all model combinations

• fitted: Actual vs fitted values for training data

• forecast: Actual vs forecasted values for test data

Examples

data_nn <- data.frame(
  y = c(
    239.7255591, 239.6504622, 239.5848569, 239.5296290,
    239.4858835, 239.4547257, 239.4372607, 239.4345936,
    239.4478298, 239.4780743, 239.5264322, 239.5940089,
    239.6819094, 239.7912389, 239.9231027, 240.0786057,
    240.2588534, 240.4649507, 240.6980029, 240.9591152,
    241.2493927, 241.5699405, 241.9218640, 242.3062682
  ),
  x1 = c(
    9.968768102, 9.160298963, 7.294994564, 5.374395163,
    4.640671747, 5.495752064, 7.155488888, 8.532368787,
    8.032804811, 10.32506916, 12.17319856, 0.571302071,
    12.20714387, 27.13871523, 35.05310057, 42.40476672,
    46.28262184, 3.089076495, 40.31650327, 20.83471700,
    25.71428597, 21.06398002, 20.26911914, 22.17299909
  ),
  x2 = c(
    0.929946922, 4.246863796, 2.895052481, 6.827712819,
    11.53788333, 5.688668709, 26.08913871, 30.14926832,
    22.77412794, 4.519550904, 18.38195203, 40.50655053,
    58.61381025, 69.95404513, 76.08779720, 86.86779542,
    79.92326273, 32.26071629, 27.67652481, 66.80672448,
    86.54120883, 97.53881465, 95.49058569, 43.06666626
  ),
  x3 = c(
    143.7114315, 153.7664088, 158.5007862, 158.7973830,
    155.8340003, 150.2453258, 142.4471949, 132.8380705,
    121.6890278, 108.8662730, 94.52734991, 78.93448337,
    62.31616514, 44.76595425, 26.34367655, 7.109157889,
    12.72227903, 32.31332405, 50.67117014, 66.80301029,
    79.71603746, 88.41744464, 92.01533759, 90.21350491
  )
)

result <- nn_model_selector(
  data = data_nn,
  response_var = "y",
  train_ratio = 0.75,
  max_nodes = 5,
  seed = 123
)

result$best_performance

autoann documentation built on Jan. 16, 2026, 1:07 a.m.