Packages, Data Import, and Tidyverse Basics

Data wrangling is the process of cleaning, transforming, and organizing raw data into a structured format suitable for analysis. This step involves handling missing values, converting data types (such as dates), filling in incomplete information, and ensuring consistency across the dataset. The main goal is to improve data quality and readability, enabling more accurate and meaningful analysis in the next stages.

Extracción de señales y pronóstico Modelo ARIMA