Sometimes we have observations at wrong frequency.
Maybe they are too fine or not fine.
And of.
For example, we may have sales data of every day and we may be trying to forecast next month's sales.
Are we really having monthly sales values and we want to project next week sales?
So sometimes the frequency of the data available does not match with the frequency at which we want
to forecast.
In such situations, we have to change the frequency of the data available with us.
This is known as resampling de data.
So in this video, you will discover how to increase and decrease the sampling frequency of the times
of this data.
After completing the section you below the two types of resampling and the main reason why you need
to use them.
As I told you, the resampling simply means changing the frequency of your time to these observations.
Now, suppose you have quarterly data and you want to forecast monthly would fall.
This means you will need to convert your quarterly data to monthly data.
This means that you will have to add samples to the data to increase the frequency.
Adding samples to data increases the frequency, and this is called Upsampling.
This is one type of resampling.
The other type is obviously downsampling in which we have more data.
See quarterly data.
And we want to make yearly predictions.
In such a scenario, we have to reduce the number of samples and therefore reduce the frequency of our
data.
For example, you can see here we had quarterly data of three years.
This is the footfall data for three years.
We have to convert this quarterly data and do eyerly data so that we can predict the footfall in it
for.
In both cases, data must be invented.
In case of Upsampling, we will calculate the fine grained observations using interpolation technique.
And in case of downsampling, we will use some statistics to calculate the new aggregate values and
the law.
We have seen only one reason of doing these sampling.
That is if the frequency of data is different from the frequency at which we want to make predictions.
We will resemble our data to that frequency and then build our model.
That is one more reason why we may do resampling, and that is for the purpose of feature engineering.
For example, suppose we have monthly sales data and we want to predict future monthly sales.
As I told you earlier in our forecasting model, we will input features.
And one of the features could be Dodo teed off last year.
Or it could be the total seed of the last day of the previous month.
These features have different frequency level than the data.
To create these features, we may do resampling.
In this way, sometimes to get features we use resembling models until.
Now let's see how to do resampling in our soffit.