This document introduces the ARIMA (AutoRegressive Integrated Moving Average) model, one of the most practical and widely used tools for business forecasting. ARIMA models capture both the autocorrelation (how current values depend on past ones) and the trends in a time series, making them ideal for short- to medium-term projections of economic or business indicators. The objective is to show how ARIMA models can help organizations anticipate changes, support strategic planning, and make informed decisions based on data-driven forecasts.

This document introduces the SARIMA (Seasonal ARIMA) approach to forecasting, a practical extension of ARIMA models widely used in business and economics. SARIMA models are designed to handle repeated seasonal patterns in data, such as quarterly demand cycles, monthly sales peaks, or yearly production slowdowns. In this example, we apply the method to forecast Mexico’s GDP growth, illustrating the same steps used in business settings: exploring the data, identifying trend and seasonality, selecting model parameters automatically, validating residuals, and producing four-quarter-ahead forecasts with confidence ranges

Actividad lúdica con motivo de los 50 años de la carrera de Planeación Territorial

Data101 Project: Using the skills I've learned from the first half of this class, specifically functions like filter, select, mutate, summary, etc I was able to showcase data from the east coast voter turnout and analyze as well as graph it.

aTest

Testing if the physical player traits of MLB shortstops from the 2025 season correlate to their defensive performance, quantified by their Baseball Savant Outs Above Average statistic.