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.
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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.