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How To Convert Non-Stationary To Stationary Time Series Data.?
How To Convert Non-Stationary To Stationary Time Series Data? Table Of Contents: Introduction. Detrending. Differencing. Transformation. (1) Introduction ‘Stationarity’ is one of the most important concepts you will come across when working with time series data. A stationary series is one in which the properties – mean, variance and covariance, do not vary with time. Let us understand this using an intuitive example. Consider the three plots shown below: In the first plot, we can see that the mean varies (increases) with time which results in an upward trend. Thus, this is a non-stationary series. For a series to be
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Why Time Series Data Need To Be Stationary?
Why Time Series Data Needs To Be Stationary ? (1) Reason-1 In Time Series data we know that the current data value is dependent on the previous data values. Which means there is a relationship between previous values and current values. If the data set is non-stationary the mean and variance of the data will change over time. As time series uses only one variable for prediction, with changing mean and variance it will be difficult to calculate the relationship between past values. Hence Time Series demands the Stationarity of data. (2) Reason-2 What quantities are we typically interested in
