Data preparation – Missing Values

This issue is the first in a series of articles that explore the data preparation aspect of time series analysis. Data preparation is often overlooked by analysts, but we believe it is a vital phase that wields a vast influence on the overall analysis and modeling process. The vast majority of time series and econometric theories assume input time series to be stationary and homogenous, with equally-spaced observations and values that are present and real. In practice, we often handle samples with missing values, unequally-spaced observations possible outliers, mean/variance dependency, restricted values ranges, and other phenomena. The aim of this series of articles is to address each of these problems and introduce practical methods to overcome them.

In this issue, we start with the sampling assumptions of the time series: equal spacing and completeness. Then we consider a time series with missing values and discuss how to represent them in Excel, with the aid of NumXL processing. Finally, we look at unequally-spaced time series, how they come into existence, how they are related to the missing values scenario, and what to do with them.

For more details and a step-by-step tutorial, along with a downloadable spreadsheet and PDF, click this link: #N/A – Missing values

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