What are the methods that can be used to imputation data of missing value?
The following are common methods:
- Mean imputation. Simply calculate the mean of the observed values for that variable for all individuals who are non-missing.
- Substitution.
- Hot deck imputation.
- Cold deck imputation.
- Regression imputation.
- Stochastic regression imputation.
- Interpolation and extrapolation.
When to reject null hypothesis in non stationary time series?
Test for stationarity: If the test statistic is less than the critical value, we can reject the null hypothesis (aka the series is stationary). When the test statistic is greater than the critical value, we fail to reject the null hypothesis (which means the series is not stationary).
Which is a function of time in a non stationary time series?
If you look at the third plot, the spread becomes closer as the time increases, which implies that the covariance is a function of time. The three examples shown above represent non-stationary time series. Now look at a fourth plot: In this case, the mean, variance and covariance are constant with time.
What does unit root mean for stationary time series?
Instead of going for the visual test, we can use statistical tests like the unit root stationary tests. Unit root indicates that the statistical properties of a given series are not constant with time, which is the condition for stationary time series. Here is the mathematics explanation of the same :
How to deal with missing values in time series?
In time series data, if there are missing values, there are two ways to deal with the incomplete data: omit the entire record that contains information. Impute the missing information. Since the time series data has temporal property, only some of the statistical methodologies are appropriate for time series data.