Is 0 considered an outlier?
Zero values that are true are usually not an outlier if the range of values falls in e.g. [0,1], [0,2] or [-2,2].
What do you do with missing values and outliers?
There are basically three methods for treating outliers in a data set. One method is to remove outliers as a means of trimming the data set. Another method involves replacing the values of outliers or reducing the influence of outliers through outlier weight adjustments.
What does having no outliers mean?
There are no outliers. Explanation: An observation is an outlier if it falls more than above the upper quartile or more than below the lower quartile. The minimum value is so there are no outliers in the low end of the distribution.
Why do we need to identify outliers?
Identification of potential outliers is important for the following reasons. An outlier may indicate bad data. For example, the data may have been coded incorrectly or an experiment may not have been run correctly. Outliers may be due to random variation or may indicate something scientifically interesting.
How to deal with missing and outliers values?
We have several solutions for imputing missing and outliers data, and we will present two of them here: If the missing or outliers values are less than 5%, you can remove the lines with missing or outliers values that will not affect your model and subsequent analysis.
Why are there missing values in real data?
Real data often contains missing values, outlying observations, and other messy features. Dealing with them can sometimes be troublesome. Missing data can arise for many reasons, and it is worth considering whether the missingness will induce bias in the forecasting model.
How are missing values related to forecasting problems?
In other situations, the missingness may be essentially random. For example, someone may have forgotten to record the sales figures, or the data recording device may have malfunctioned. If the timing of the missing data is not informative for the forecasting problem, then the missing values can be handled more easily.
What to do when missing values cause errors?
When missing values cause errors, there are at least two ways to handle the problem. First, we could just take the section of data after the last missing value, assuming there is a long enough series of observations to produce meaningful forecasts. Alternatively, we could replace the missing values with estimates.