A basic assumption taken into consideration when performing analytics on time series data using various models is that the series is stationary. If the original data series is found to be non-stationary then we proceed to apply transformations to get optimum stationary series data. We took a random time series and applied first and second-order differentiation to show how transformations can affect stationarity.
Explore more about the importance of stationarity in time series prediction follow the link below:
https://towardsdatascience.com/stationarity-in-time-series-analysis-90c94f27322
https://towardsdatascience.com/stationarity-in-time-series-analysis-90c94f27322
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