Predictor provides two methods for filling in missing values and adjusting outliers:
Missing values at the beginning of a data series are ignored. Missing values at the end of a data series are allowed, but this condition is not ideal. The cubic spline interpolation method is especially sensitive to data missing at the end of the series. If one or two values are missing, cubic spline interpolation can be used. If multiple values are missing, neighbor interpolation provides a better estimate.
|
Tip: |
An obvious outlier, such as a large data spike, should be replaced by a blank cell in the original data set. Otherwise, neighbor interpolation is probably a better adjustment method, especially if the specified neighbors do not include the spike. Because cubic spline interpolation takes into account the whole data set, that adjustment method will be affected by the outlier. |
Cubic spline interpolation is based on a drafting tool used to draw smooth curves through a number of points. The spline tool consists of weights attached to a flat surface at the points to be connected. A flexible strip is then bent across each of these weights, resulting in a smooth curve.
The cubic spline interpolation is a piecewise continuous curve that passes through each value in the data set. Each interval of the curve has a separate cubic polynomial, each with its own coefficients. These are equivalent to the spline tool's weights. The cubic spline interpolation method considers the entire data set when adjusting outliers and filling in missing values.
This method is also called the single imputation method. A certain number of neighbors on each side of the missing value are considered when estimating the missing value. This value is called n, and the total number of neighbors evaluated is 2n. The missing data is replaced by the mean or median of the 2n data points. The default value for n is 1, and the default statistic is Mean.
|
Note: |
To preserve the local nature of time series data, the number of neighbors on each side of the missing value or values should be small, ideally n = 1 or n = 2. |