Hi Shuling,
You can add statistical features to a 1D CNN in MATLAB where the input is time-series data. MATLAB provides various functions and techniques to compute statistical features that can be incorporated into your CNN model.
Here's a general approach to incorporate statistical features into a 1D CNN in MATLAB:
- Preprocess your time-series data: Prepare your time-series data by normalizing or scaling it appropriately. You can use techniques like min-max scaling or z-score normalization to ensure that the data is within a suitable range.
- Extract statistical features: Compute statistical features from your time-series data. Some commonly used statistical features for time-series data include mean, standard deviation, variance, skewness, kurtosis, and various percentiles. MATLAB provides functions such as "mean", "std", "var", "skewness", "kutosis", and "prctile" for calculating these features.
- Prepare the input data: Combine the original time-series data with the extracted statistical features. You can create a new feature vector by concatenating the original time-series data with the computed statistical features.
- Build the 1D CNN model: Design and build your 1D CNN model using MATLAB's Deep Learning Toolbox. Specify the appropriate input size to accommodate the combined feature vector.
- Train and evaluate the model: Split your dataset into training, validation, and testing sets. Train the 1D CNN model using the training data and evaluate its performance on the validation and testing data. Monitor metrics such as accuracy, loss, and validation error to assess the model's performance.
- Test the model: Once the model is trained and evaluated, you can use it to make predictions on new, unseen time-series data. Preprocess the new data, extract the statistical features, and pass it through the trained model to obtain predictions.
You can also explore additional techniques such as data augmentation, regularization, and hyperparameter tuning to further enhance the performance of your 1D CNN model.
Remember to adjust the architecture and hyperparameters of the CNN model based on your specific problem and dataset to achieve optimal results.
I hope this addresses your query.
