Description: A univariate time series is a set of observations of a single variable over time. This type of series is characterized by its sequential nature, where each data point is associated with a specific moment, allowing for the analysis of how the variable changes over time. Univariate time series are fundamental in data analysis as they facilitate the identification of patterns, trends, and cycles in the observed variable. For example, they can be used to study daily temperature behavior, stock prices in the financial market, or the number of visitors to a website. Visualizing these series is crucial for data interpretation, and tools in data analysis environments allow for the creation of graphs that effectively represent these variations. Through line graphs, histograms, or scatter plots, analysts can gain a deeper understanding of the dynamics of the variable over time, which is essential for informed decision-making in various fields, from economics to meteorology.
