Functional Factor Models: Forecasting Mortality Curves in Japan

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Page 3 of 4

Explores forecasting methods, professional predictions, biases, and challenges in economic trend forecasting.

Introduces Principal Component Analysis, focusing on maximizing variance in linear combinations to summarize data effectively.

Explores PCA and t-SNE for reducing dimensions and visualizing high-dimensional data effectively.

Explores PCA for predicting medals distribution and compressing face images.

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

Explores multivariate time series analysis, cointegration, forecasting with ARMA models, and practical applications in interest rates analysis.

Explores Principal Component Analysis for dimensionality reduction and unsupervised feature selection.

Covers demand management optimization, focusing on forecasting methods and model initiation.

Covers the application of Principal Component Analysis in facial recognition using a famous faces dataset.

On PCA includes interactive exercises and emphasizes minimizing information loss.