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What is an example of a stationary process?

Published in Statistics 1 min read

A stationary process is a time series where the statistical properties, such as mean, variance, and autocorrelation, remain constant over time.

Here's an example:

  • The daily temperature of a city: Imagine the average daily temperature in a city like London. While there will be fluctuations throughout the year, the overall average temperature, variance, and correlation between different days remain relatively consistent over long periods.

In contrast, a non-stationary process would exhibit changes in these statistical properties over time. For example, the price of a stock over a few years would be a non-stationary process, as the mean and variance can change significantly due to market fluctuations.

Stationary processes are important in various fields, including:

  • Time series analysis: They allow for easier forecasting and modeling of future values.
  • Signal processing: They are used in filtering and analyzing signals.
  • Control systems: They are essential for designing feedback loops.

Understanding stationary processes is crucial for analyzing and interpreting time series data.

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