Intervention Analysis In Time Series
- Identifies and analyzes how a planned change alters trends and patterns in time series data.
- Chooses between parametric methods (which assume trend or seasonality) and non-parametric methods (which make no structural assumptions).
- Typically compares measurements before and after an intervention to determine its effect.
Definition
Section titled “Definition”Intervention analysis is a statistical method used to identify and analyze the effects of interventions on time series data. An intervention is a planned change in the system being studied, such as a change in policy or the introduction of a new treatment. The goal of intervention analysis is to understand how these interventions affect the underlying trends and patterns in the data.
Explanation
Section titled “Explanation”There are two main types of intervention analysis: parametric and non-parametric. Parametric methods make assumptions about the underlying structure of the data, such as the presence of a trend or seasonal pattern. Non-parametric methods do not make any assumptions about the data, and can be applied to a wider range of data sets.
Intervention analysis is applied by examining time series measurements before and after a planned change to determine whether the intervention produced the desired effect on the observed series.
Examples
Section titled “Examples”Economics
Section titled “Economics”Policy makers use time series data to understand the effects of government interventions on economic indicators such as GDP and unemployment. For example, if a government introduces a new policy to stimulate economic growth, intervention analysis can be used to analyze GDP time series data before and after the policy change to determine whether the intervention had the desired effect.
Medicine
Section titled “Medicine”Doctors and researchers use time series data to understand the effects of interventions on patient health. For example, if a researcher studies the effects of a new medication on blood pressure, intervention analysis can be used to analyze blood pressure time series data before and after the medication is introduced to determine whether the intervention had the desired effect on the patient’s health.
Use cases
Section titled “Use cases”- Evaluating policy changes in economics (e.g., effects on GDP and unemployment).
- Assessing clinical or treatment interventions in medicine (e.g., effects on patient blood pressure).
Related terms
Section titled “Related terms”- Time series
- Trend
- Seasonal pattern
- Parametric methods
- Non-parametric methods
- Intervention