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3. Autocorrelation and partial autocorrelation
Autocorrelation is a powerful analysis tool for modeling time series data. As the name suggests, it involves computing the correlation coefficient. But here, rather than computing it between two features, correlation of a time series is found with a lagging version of itself.
Let’s first look at an example plot and explain further:
The XAxis of an autocorrelation function plot (ACF) is the lag number k. For example, when k=1, the correlation is found by shifting the series by 1. This is the same as using the
shift function of Pandas:
The YAXis is the amount of correlation at each lag k. The shaded red region is a confidence interval — if the height of the bars is outside this region, it means the correlation is statistically significant.
Please pause and think of what you can learn from an ACF plot.
They offer an alternative way of detecting patterns and seasonality. For example, the ACF plot of temperature in Celcius shows that the correlation at every 15 lags decreases or every 25 lags increases.
When a clear trend exists in a time series, the autocorrelation tends to be high at small lags like 1 or 2. When seasonality exists, the autocorrelation goes up periodically at larger lags.
Let’s look at another example:
The ACF of carbon monoxide confirms that small lags tend to have high correlations. It also shows that every 25 lags, the correlation increases significantly but quickly drops down to the negative. But most of the downward bars are inside the shaded area, suggesting that they are not statistically significant.
This ability to compare the relationship between past and present data points present a unique advantage. If you can associate the present value to points k periods before, this also means you can find a link to values that come after k periods.
Besides, understanding autocorrelation is key to modeling time series with ARIMA models (a topic for another article).
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