Is the #Bitcoin price a stationary times series? ๐ค
Check ๐ how to find it out!
#DataScience #MachineLearning
Check ๐ how to find it out!
#DataScience #MachineLearning
We know that stationary means that the mean and variance of the time series data do not vary across time.
To be sure of that we can perform the Augmented Dickey-Fuller test.
To be sure of that we can perform the Augmented Dickey-Fuller test.
If you are familiar with #statistics here you have the hypotheses for this test:
H0๏ธโฃ (Null hypothesis) = Time series non-stationary
H1๏ธโฃ (Alternative hypothesis) = Time series is stationary
After the test, we will pay attention to the "p-value".
H0๏ธโฃ (Null hypothesis) = Time series non-stationary
H1๏ธโฃ (Alternative hypothesis) = Time series is stationary
After the test, we will pay attention to the "p-value".
This is how to perform the test in ๐ #Python
You can import it from the statsmodels library.
If you don't have it in your environment, you can get it using "pip install statsmodels"
You can import it from the statsmodels library.
If you don't have it in your environment, you can get it using "pip install statsmodels"
We get lots of numbers out of this test, but which one should we care about?
We need to compare the second number, which corresponds to the p-value (0.5093) with our significance level ฮฑ. This is generally set to 0.05.
We need to compare the second number, which corresponds to the p-value (0.5093) with our significance level ฮฑ. This is generally set to 0.05.
We will have two scenarios:
1๏ธโฃ p-value > 0.05 : we fail to reject the null hypothesis, which means that our time series is non-stationary.
2๏ธโฃ p-value < 0.05 : we reject the null hypothesis, which for us means that our time series can be considered stationary.
1๏ธโฃ p-value > 0.05 : we fail to reject the null hypothesis, which means that our time series is non-stationary.
2๏ธโฃ p-value < 0.05 : we reject the null hypothesis, which for us means that our time series can be considered stationary.
In our case, the p-value is 0.5093.
It is greater than ฮฑ = 0.05 โ our time series data is non-stationary... ๐ข
It is greater than ฮฑ = 0.05 โ our time series data is non-stationary... ๐ข
What if we do the same but with the price returns instead of with the price? ๐ค
We can calculate the returns as the percentage change.
We can calculate the returns as the percentage change.
This time the p-value is 0.0.
Which is lower than ฮฑ = 0.05 โ our time series data is stationary! ๐ฅณ
Which is lower than ฮฑ = 0.05 โ our time series data is stationary! ๐ฅณ
This means that we can use an ARMA model to forecast the returns.
Also, we can use an ARIMA model to forecast the prices if we use d=1.
Also, we can use an ARIMA model to forecast the prices if we use d=1.
https://twitter.com/daansan_ml/status/1590319971287998465?s=20&t=H6PTmUsrOM3MvRMhMyz1kw
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Please support my content with a Retweet & Like on the first tweet of this thread.
Thanks!๐
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