MLP

MLP (Multi-Layer Perceptron) is one of the most simpliest neutral network model. It can capture non-linear relationships between features and target and also find patterns by identifying features automatically.

MLP architecture

We use multi-layer percenptron with ReLU as activation function so that features can be discovered. Dropouts are added between layers to prevent overfitting.

MLP for Air Passengers

To demonstrate model performance, we show the model's prediction results for the air passengers dataset. The cross validation process identified the best transformation to make the timeseries stationary and the optimal hyperparameters. The Root Mean Squared Error on the next day's closing price was used to determine the best model.

The chart below illustrates:

1. train: training data
2. prediction(train): the model's prediction for the training data periods
3. test(input): test input data
4. test(actual): test actual data
5. prediction(test): the model's prediction on the selected days (1, 2, 7th days) of "test (actual)" periods given "test (input)".

MLP predicts the increases percentage of air passengers as described in capturing trends for prediction. Number of hidden layers, dimensions, dropout rates, etc. were determined by grid search.