[Time Series Anomaly Detection, Feature Engineering, Statistical Learning Modeling, Deep Learning Modeling]
· Data Transformation (Logarithm, Box-Cox)
· Feature Scaling (Rescaling, Standardization, Robust Scaling, L2 Normalization)
· Dimensionality Reduction (PCA, LDA, t-SNE, UMAP)
· Statistical Learning Modeling (Isolation Forest, One-Class SVM)
· Deep Learning Modeling (LSTM, GRU, CNN, Autoencoder, VAE)
· Deep Learning Optimization (Layer Details, Batch Size, Learning Rate, Node, Optimizer Function, Pruning, Stateful, Callback, Activation Function)
· Classification Evaluation Metrics (MSE and MAE for Loss Function, Accuracy, Precision and TPR Recall, TPR Sensitivity and TNR Specificity, F-Score, ROC Curve, RMSE, MAPE, Kappa)
[Anomaly Detection, Vibration Analysis, Statistical Learning Modeling, Deep Learning Modeling]
· Vibration Analysis (Spectrogram, Fast Fourier Transform, Wavelet Transform, Power Spectral Density)
· Vibration Data Labeling (Statistical Dynamic Threshold)
· Statistical Learning Modeling (Isolation Forest, One-Class SVM)
· Deep Learning Modeling (LSTM, GRU, CNN, Autoencoder, VAE)
· Deep Learning Optimization (Layer Details, Batch Size, Learning Rate, Node, Optimizer Function, Pruning, Stateful, Callback, Activation Function)
· Classification Evaluation Metrics (MSE and MAE for Loss Function, Accuracy, Precision and TPR Recall, TPR Sensitivity and TNR Specificity, F-Score, ROC Curve, RMSE, MAPE, Kappa)
