How can I build and train a MLP network for time series prediction?

Hello to all dear friends.

For my project I need to use an MLP neural network to predict future rainfall by using relative humidity dataset. Also, I am very inexperienced in using MATLAB. Please guide me to do this project.

Thank you in advance

Answer 1

[atharva]

Hey Mir Sahand,

I understand that you want to use a MLP neural network to predict future rainfall by using relative humidity dataset.

Please go throught the following steps-

1. Load and Preprocess Data: Load your dataset containing relative humidity and rainfall data. Split the dataset into training and testing sets.

2. Normalize Data: Normalize the data to ensure that the input features are on a similar scale. You can use the Normalize function.

3. Build and Train MLP Model: Define and configure the MLP model using the Neural Network Toolbox in MATLAB. You can use the Train function.

4. Make Predictions: Use the trained model to make predictions on the test set.

5. Evaluate the Model: Evaluate the performance of the model using appropriate metrics.

This is a basic guide, and you may need to adjust parameters such as the number of hidden layers, learning rate, and training epochs based on your dataset and the characteristics of your problem.

I hope this helps!

[Mir Sahand]

@atharva

Many Thanks for your response but my main problem here is that i dont know how to open neural network toolbox and how to use it in order to have a MLP model so after that I can train it. also, is it possible to use 5 inputs (relative humidity, temperature and ...) instead of just one? how can i set that up? can you guide me on this matters?

Thanks in advance!