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Remember to also consider the stationarity of your time series data. **Stationarity** means that the statistical properties of your data (mean, variance) do not change over time. Many time series models, including the ARDL, assume that your data is stationary. If your data is non-stationary, you may need to apply transformations, such as differencing, to make it stationary. Differencing involves calculating the difference between consecutive observations. For example, if you have sales data, you might take the difference between this month's sales and last month's sales. The use of differencing can also alter the lag structure, so keep this in mind when developing your models.
Okay, let's pretend that **Oleg** is a real character. Oleg would probably have some awesome skills! Let's talk about what the skills would be.
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