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Sometimes we have observations at wrong frequency.

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Maybe they are too fine or not fine.

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And of.

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For example, we may have sales data of every day and we may be trying to forecast next month's sales.

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Are we really having monthly sales values and we want to project next week sales?

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So sometimes the frequency of the data available does not match with the frequency at which we want

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to forecast.

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In such situations, we have to change the frequency of the data available with us.

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This is known as resampling de data.

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So in this video, you will discover how to increase and decrease the sampling frequency of the times

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of this data.

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After completing the section you below the two types of resampling and the main reason why you need

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to use them.

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As I told you, the resampling simply means changing the frequency of your time to these observations.

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Now, suppose you have quarterly data and you want to forecast monthly would fall.

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This means you will need to convert your quarterly data to monthly data.

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This means that you will have to add samples to the data to increase the frequency.

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Adding samples to data increases the frequency, and this is called Upsampling.

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This is one type of resampling.

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The other type is obviously downsampling in which we have more data.

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See quarterly data.

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And we want to make yearly predictions.

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In such a scenario, we have to reduce the number of samples and therefore reduce the frequency of our

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data.

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For example, you can see here we had quarterly data of three years.

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This is the footfall data for three years.

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We have to convert this quarterly data and do eyerly data so that we can predict the footfall in it

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for.

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In both cases, data must be invented.

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In case of Upsampling, we will calculate the fine grained observations using interpolation technique.

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And in case of downsampling, we will use some statistics to calculate the new aggregate values and

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the law.

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We have seen only one reason of doing these sampling.

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That is if the frequency of data is different from the frequency at which we want to make predictions.

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We will resemble our data to that frequency and then build our model.

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That is one more reason why we may do resampling, and that is for the purpose of feature engineering.

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For example, suppose we have monthly sales data and we want to predict future monthly sales.

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As I told you earlier in our forecasting model, we will input features.

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And one of the features could be Dodo teed off last year.

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Or it could be the total seed of the last day of the previous month.

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These features have different frequency level than the data.

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To create these features, we may do resampling.

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In this way, sometimes to get features we use resembling models until.

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Now let's see how to do resampling in our soffit.