A univariate time series consists of a sequence of observations collected over time, focusing solely on one variable. This means that at each time point, only a single value or measurement is recorded, which makes it easier to analyze patterns, trends, and seasonal variations related to that specific variable. For example, tracking daily temperature readings in a city is a classic example of a univariate time series, where each day’s temperature is the only data point being monitored. Other examples could include stock prices of a single company or monthly sales figures for a particular product.
In contrast, a multivariate time series involves multiple variables recorded at the same time intervals. This allows for the analysis of complex relationships between different variables over time. For instance, if we consider the same stock market example, a multivariate time series could include not only the stock prices of a company but also related metrics such as trading volume, market indices, and economic indicators. This combination of data enables a more comprehensive understanding of the factors influencing the main variable and the interactions between them.
The choice between univariate and multivariate time series depends on the analysis goals. If the focus is solely on understanding the behavior of one variable over time, the univariate approach is sufficient. However, when it's crucial to understand how multiple interrelated factors influence each other, a multivariate analysis is more appropriate. This added complexity requires more sophisticated modeling techniques and tools, but the insight gained can be invaluable for making informed decisions.