Multiple Time Series
- Multiple time series are collections of related time-indexed variables analyzed together to study their relationships.
- They enable comparison of trends and patterns across variables to gain broader insights than single-series analysis.
- Analysis often uses methods like regression, decomposition, and cross-correlation, and requires attention to multicollinearity (e.g., via PCA or variable selection).
Definition
Section titled “Definition”A multiple time series is a type of time series data that involves more than one dependent variable. It is a collection of time series data sets that are related to each other and have a common time frame.
Explanation
Section titled “Explanation”Multiple time series let analysts examine relationships between different variables observed over the same time period. By plotting and comparing each series, one can identify trends, patterns, and interdependencies that are not visible from a single time series alone. Standard statistical techniques for analyzing multiple time series include regression analysis, time series decomposition, and cross-correlation analysis. When variables are highly correlated, multicollinearity can complicate interpretation; techniques such as principal component analysis or variable selection methods can be used to address redundant or irrelevant variables.
Examples
Section titled “Examples”Competing companies’ stock prices
Section titled “Competing companies’ stock prices”The stock prices of two competing companies in the same industry can form a multiple time series. Each company’s stock-price series can be plotted and compared over time to examine relative performance and factors driving their prices.
Sales data for different product lines
Section titled “Sales data for different product lines”A company’s sales data across different product lines is another multiple time series. Each product line’s sales series can be plotted and compared to assess relative popularity and identify potential areas for growth or improvement.
Use cases
Section titled “Use cases”- Gaining insights into relationships between variables observed over the same time frame.
- Making more informed managerial or analytical decisions based on comparative trends.
- Making predictions about future outcomes using multivariate time series techniques.
Notes or pitfalls
Section titled “Notes or pitfalls”- Multicollinearity: when two or more variables are highly correlated, it can be difficult to determine individual effects on the outcome.
- Mitigation techniques mentioned: principal component analysis and variable selection methods.
Related terms
Section titled “Related terms”- Time series
- Regression analysis
- Time series decomposition
- Cross-correlation analysis
- Multicollinearity
- Principal component analysis
- Variable selection methods