Autocorrelation plot intuitive

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I am analyzing a time series dataset and I used seasonal_decompose function in statsmodel library to obtain trend and seasonal behavior. I obtained the autocorrelation plot and the decomposition of the time-series provided should provide a “remainder” component that should be uncorrelated. By observing the autocorrelation plot how do we say that auto-correlation function indicate that the remainder is indeed uncorrelated?

I am attaching the code I used to obtain autocorrelation plot and the plot obtained.

fig, ax = plt.subplots(figsize=(20, 5))
plot_acf(data, ax=ax)
plt.show()

Autocorrelation_plot

autocorrelation statistics python
1 Answers

if the results of auto correlation are close to zero then the features not not correlated. I use lag of 40, but you will need to adjust this value dependant on your data.

plt.clf()
fig,ax = plt.subplots(figsize=(12,4))
plt.style.use('seaborn-pastel')
fig = tsaplots.plot_acf(df['value'], lags=40,ax=ax)
plt.show()
print('values close to 1 are showing strong positive correlation. The blue regions are showing areas of uncertainty')
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