Other dimensionality reduction methods like Linear Discriminant Analysis (LDA) or Independent Component Analysis (ICA) can complement the effectiveness of Principal Component Analysis (PCA) in enhancing transformer-based forecasting by offering different perspectives on feature extraction and reduction.
Linear Discriminant Analysis (LDA): LDA focuses on finding the linear combinations of features that best separate classes in a dataset. By incorporating LDA alongside PCA, we can potentially enhance the interpretability of the transformed features, especially when dealing with classification tasks within time series forecasting. This combination could help identify key discriminative features that contribute significantly to predictive accuracy.
Independent Component Analysis (ICA): ICA aims to extract statistically independent components from multivariate data. When used in conjunction with PCA, ICA can capture non-Gaussian dependencies between variables that may be missed by PCA alone. This approach could lead to a more comprehensive representation of underlying patterns in the data, improving model performance and robustness.
By integrating these additional dimensionality reduction techniques into the framework alongside PCA, we can leverage their unique strengths to further refine feature representations for transformer-based forecasting models.