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Whether it’s search demand, revenue, or traffic from organic search, at some point in your SEO career, you’re bound to be asked to deliver a forecast.
In this column, you’ll learn how to do just that accurately and efficiently, thanks to Python.
We’re going to explore how to:
- Pull and plot your data.
- Use automated methods to estimate the best fit model parameters.
- Apply the Augmented Dickey-Fuller method (ADF) to statistically test a time series.
- Estimate the number of parameters for a SARIMA model.
- Test your models and begin making forecasts.
- Interpret and export your forecasts.
Before we get into it, let’s define the data. Regardless of the type of metric, we’re attempting to forecast, that data happens over time.
In most cases, this is likely to be over a series of dates. So effectively, the techniques we’re disclosing here are time series forecasting techniques.
So Why Forecast?
To answer a question with a question, why wouldn’t you forecast?
These techniques have been long used in finance for stock prices, for example, and in other fields. Why should SEO be any different?
With multiple interests such as the budget holder and other colleagues – say, the SEO manager and marketing director – there will be expectations as to what the organic search...
Read Full Story: https://www.searchenginejournal.com/python-seo-forecasting/420237/
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