Univariate and multivariate time series are two approaches to analyzing time-dependent data, and the key difference lies in the number of variables being considered. A univariate time series consists of observations collected from a single variable over time. For example, if you track the daily temperature of a city, that data represents a univariate time series. The analysis focuses solely on how that one variable changes over different time periods, making it simpler and often easier to model.
In contrast, a multivariate time series involves multiple variables that may interact with one another over time. For example, if you are analyzing the sales of a product alongside marketing spend and consumer sentiment, you are dealing with a multivariate time series. Each of these variables can affect the others, and the analysis aims to uncover the relationships and patterns among them. This complexity can lead to richer insights but also requires more advanced statistical methods and tools for effective modeling and interpretation.
When choosing between univariate and multivariate approaches, developers and analysts must consider the research question and the nature of the data. Univariate analysis is often sufficient for straightforward trends or seasonal patterns, while multivariate analysis is essential when understanding interactions between different variables is necessary. For instance, forecasting a stock price based solely on historical prices might use a univariate model, but predicting it while considering interest rates and market trends would require a multivariate approach. Thus, selecting the right method can significantly impact the quality of the results.