Package 'autoann' reference manual

Title:	Neural Network–Based Model Selection and Forecasting
Description:	Provides a systematic framework for neural network–based model selection and forecasting using single hidden layer feed-forward networks. It evaluates all possible combinations of predictor variables and hidden layer configurations, selecting the optimal model based on predictive accuracy criteria such as root mean squared error (RMSE) and mean absolute percentage error (MAPE). Predictors are automatically standardized, and model performance is assessed using out-of-sample validation. The package is designed for empirical modelling and forecasting in economics, agriculture, trade, climate, and related applied research domains where nonlinear relationships and robust predictive performance are of primary interest.
Authors:	Dr. Pramit Pandit [aut, cre], Ms. Moumita Paul [aut], Dr. Bikramjeet Ghose [aut]
Maintainer:	Dr. Pramit Pandit <[email protected]>
License:	GPL-3
Version:	0.1.0
Built:	2026-05-23 15:10:03 UTC
Source:	https://github.com/cran/autoann

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
)
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

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

Package 'autoann'