ExamNumbers

Trabajo final

Forecasting the classic AirPassengers time series with ETS, ARIMA, NNAR and Prophet. Includes full R code, hold-out metrics and practical recommendations.

This analytic tool provides a GLS-based within-study meta-analytic method to combine multiple correlation coefficients from a single study—accounting for the statistical dependency between them to produce a single pooled correlation estimate. It uses the generalized least squares (GLS) framework, weighting each correlation by its precision and accounting for their inter-correlations via a covariance matrix.

This project investigates the evaluation and comparison of different recommender algorithms, implements a business-relevant goal (diversity), and critically reflects on the challenges of offline evaluation—drawing on *Building a Recommendation System in R* (Chapters 4 and 5) and real-world practices.

Time series and production functions