Advanced Data Analytics Using Python_ With Machine Learning, Deep Learning and NLP Examples ( 2023)

Chapter 6
Time Series
To Make the Seasonal Effect Additive
If the series has a trend and the volume of the seasonal effect appears to be
on the rise with the mean, then it may be advisable to modify the data so as
to make the seasonal effect constant from year to year. This seasonal effect
is said to be additive. However, if the volume of the seasonal effect is directly
proportional to the mean, then the seasonal effect is said to be multiplicative,
and a logarithmic transformation is needed to make it additive again.
To Make the Data Distribution Normal
In most probability models, it is assumed that distribution of data is
Gaussian or normal. For example, there can be evidence of skewness in a
trend that causes “spikes” in the time plot that are all in the same direction.
To transform the data in a normal distribution, the most common
transform is to subtract the mean and then divide by the standard
deviation. I gave an example of this transformation in the RNN example in
Chapter 5; I’ll give another in the final example of the current chapter. The
logic behind this transformation is it makes the mean 0 and the standard
deviation 1, which is a characteristic of a normal distribution. Another
popular transformation is to use the logarithm. The major advantage of a
logarithm is it reduces the variation and logarithm of Gaussian distribution
data that is also Gaussian. Transformation may be problem-specific or
domain-specific. For instance, in a time series of an airline’s passenger
load data, the series can be normalized by dividing by the number of days
in the month or by the number of holidays in a month.
Cyclic Variation
In some time series, seasonality is not a constant but a stochastic variable.
That is known as cyclic variation. In this case, the periodicity first has to
be predicted and then has to be removed in the same way as done for
seasonal variation.
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