Dashboards are among the most powerful tools in business intelligence and data science because they transform raw data into accessible, decision-ready insights. In this part of the course, we will learn how to design and build reactive dashboards that respond instantly to user input.

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

In the field of business intelligence time series analysis plays a central role because much of the data that organizations generate and monitor is time-dependent, such as sales, customer demand, or economic/financial indicators. In this module, we divide our work into two sections. In the first section, we focus on the fundamentals. In the second section, we turn to forecasting, applying statistical and machine learning methods to predict future values.