Why use autocorrelation instead of autocovariance when examining stationary time

Question

Why use autocorrelation instead of autocovariance when examining stationary time series? Why use autocorrelation instead of autocovariance when examining stationary time series? PRINTED BY: nelsonsarin@gmail.com. Printing is for personal, private use only. No part of this book may be reproduced or transmitted without publisher's prior permission. Violators will be prosecuted. nter-Chater Distanees Chater 32.574 Clate 1 Chster 2 4 Jay Glacby categoines wines into one of thrce claiers. The cenoids of these claies desctihing the sverage characteniics of a win in cach chaviker are listol is che Cluater 1 Cluster 2 0035 0.977 -1.212 0.825 0083 029 -0778 Jay has recently discovered a new wise from the Phiodon oepion of luly with the fellowing choracteristics In which clusier of wines should he place this new wine? Justify your chsice with appropriate calcolations -1.242 1.094 0.001 0.229 Proarthocyanins 0711 -0 425 0.010

Explanation / Answer

When using linear programming the assumption is taken that the data would not change much. As a result of which in such cases the residual error which is caused and might create inconsistency in the final result is hard to eliminate.

The use of autocovariance leads the change of the variables as the change of data occurs with time and leads to incorrect results. Hence in case, the data does not change much with time the as in case of stationary time series and hence autocovariance is better applicable there. In case the data changes with autocorrelation are better applicable in such scenarios.

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