[Time series] MATLAB implements DNN deep neural network time series to predict the future

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MATLAB implements DNN deep neural network time series to predict the future

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MATLAB implements DNN deep neural network time series to predict the future

MATLAB can realize the application of DNN deep neural network in time series forecasting through Deep Learning Toolbox. The following are the specific steps:

1. Prepare the data set: Arrange the time series data set in chronological order and divide it into training set and test set.

2. Create a model: Use Deep Learning Toolbox in MATLAB to create a DNN deep neural network model, select appropriate hyperparameters such as network structure, activation function, and optimizer, and perform model training based on the training set.

3. Model evaluation: Use the test set to evaluate the trained model, and the evaluation index can use indicators such as root mean square error (RMSE).

4. Apply the model to predict: input the historical data in the time series into the trained model, and you can get the prediction result of the future time point.

It should be noted that in the practical application of time series forecasting, it is often necessary to tune and optimize the model to improve the forecasting accuracy of the model and reduce overfitting. <