Thread by @rohanpaul_ai

1/ One way to test whether a time series is stationary is to perform an augmented Dickey-Fuller test - A Thread

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2/ H0: The time series is non-stationary. In other words, it has some time-dependent structure and does not have constant variance over time.

HA: The time series is stationary.

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3/ If the p-value from the test is less than some significance level (e.g. α = .05), then we can reject the null hypothesis and conclude that the time series is stationary.

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4/ Example: Augmented Dickey-Fuller Test in Python

Suppose we have the following time series data and first we create a quick plot to visualize the data

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5/ To perform an augmented Dickey-Fuller test, we can use the adfuller() function from the statsmodels library. First, we need to install statsmodels:

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6/ Here’s how to interpret the most important values in the output:

• Test statistic: -0.97538
• P-value: 0.7621

Since the p-value is not less than .05, we fail to reject the null hypothesis

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7/ This means the time series is non-stationary. In other words, it has some time-dependent structure and does not have constant variance over time.

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