Trying to underst Correlation

Yesterday’s example made me think a lot. Below is equation for a correlation coefficient between two data sets:

Przechwytywanie

At first I thought that in very simple words if correlation equals 1 then trend lines are equal. Let’s have a quick look on a very simple example:

Przechwytywanie

This two primitive data sets are having very similar trend lines, however their correlation is only 0,4. Another example:

Przechwytywanie

Numbers in two data sets are scaled. Correlation equals 1.

And a final example:

Przechwytywanie

Very high correlation at the level of 0,91. Interesting.

I do understand why in yesterday’s example it didnt make sense to correlate daily changes, which would end up in completly irrelevant and non correlated trend lines.

It’s time to dig more into time series forecasting. Quick google resulted in a blog post I want to analyze in comming days: https://towardsdatascience.com/bitcoin-price-prediction-using-time-series-forecasting-9f468f7174d3 and this one: https://machinelearningmastery.com/arima-for-time-series-forecasting-with-python/

Thanks for reading,
Łukasz.

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