I learned about one of the fundamental concepts used in time series analysis in Python that is, autocorrelation. They are the measure of the correlation between a time series and a lagged version of itself which means that it measures how similar a data point is to the previous data point(s) in the `series`

. However, I am unaware of how you can calculate this correlation of a numeric Pandas `series`

, please provide me with some of the most efficient techniques available along with a simple code example that will help me solve my problem.

Hey @mubashir_rizvi, You can use the `corr()`

method to find the autocorrelation between the original series and the shifted series created with the help of the `shift()`

method. The `shift()`

method is used for shifting the index of a time series forward or backward by a specified number of periods called lag.

Hi @mubashir_rizvi You can use `np.corrcoef()`

function, which computes the Pearson correlation coefficient between two arrays, or, as in this example, between two series.

We computed the correlation between the original time series and a lagged version of itself where the lag is determined by slicing the series using `seriess[:-lag]`

and `series[lag:]`

using a `lag`

of value `1`

.

To compute the autocorrelation between consecutive subsets of a time series, you can use the `rolling()`

method in pandas. then specify a window size of 2 using the `window`

parameter. The `corr()`

method is then applied to calculate the correlation between the rolling window and a lagged version of the time series. To shift the time series, you can use the `shift()`

method, which moves the index forward or backward by a specified number of periods, or lags. By default, the `shift()`

method shifts the index by 1 period.