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In this article, we’re diving into the critical concepts of unit roots and stationarity. Buckle up for an exploration into why checking stationarity is crucial, what unit roots are, and how these elements play a key role in our predictive maintenance arsenal. We will also master the chaos!
This article is part of the series Understanding Predictive Maintenance. I plan to create the entire series in a similar style.
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Ever wondered if your data is playing a game of hide and seek? Let’s cut to the chase — we’re talking about stationarity. It’s not just a fancy term; it’s the secret sauce to understanding how stable and predictable your time-dependent data really is. Buckle up as we explore why data stationarity is the game-changer in modeling and forecasting.
Key Rules of Stationarity
- Constant Mean: A stationary time series should exhibit a consistent average value over time. If the mean changes, it suggests a shift in the underlying behavior of the process.
- Constant Variance: The variance of the time series, representing the spread of data points, should remain constant. Fluctuations in variance can make it challenging to make accurate predictions.
- Constant Autocorrelation: Autocorrelation measures the correlation between a time series and its lagged values. In a stationary series, the strength and pattern of autocorrelation should be consistent throughout.
Just “stability” of statistical properties.
Why Stationarity is a Big Deal
Imagine your predictive models as expert navigators sailing through the sea of data. To navigate smoothly, they prefer calm waters — that’s where stationarity comes in. Stationary data is like a serene…
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