Reducing autocorrelation of a time series

In summary, there is no set threshold for autocorrelation time and sub-sampling is one method that can be used to reduce autocorrelation in a time series.
  • #1
iibewegung
16
0
Hi,
Is there like some widely accepted threshold for autocorrelation time of a time series for it to be considered "uncorrelated"?
I used MATLAB to generate approx. 1000 gaussian random numbers and found their autocorrelation time to be approx. 1.07... is this small enough for it to be called white noise?

If not, can anyone tell me some ways to reduce the autocorrelation of a simple time series?
I heard sub-sampling is one way... is there anything else?
If someone can perhaps tell me the name of the method, I think I can look it up and do the rest.

Thanks in advance.
 
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  • #2
There is no widely accepted threshold for autocorrelation time of a time series to be considered "uncorrelated". It is usually determined on a case-by-case basis, depending on what you are trying to achieve with the data. Sub-sampling is one method that can be used to reduce the autocorrelation of a simple time series. Other methods include differencing, smoothing, and detrending. These methods work by removing patterns from the data that are causing the autocorrelation. For more information, you can read about time series analysis and the various methods for reducing autocorrelation.
 

Related to Reducing autocorrelation of a time series

1) What is autocorrelation in a time series?

Autocorrelation in a time series refers to the correlation between a variable and its past values. It measures the degree to which the current value of a variable is related to its previous values.

2) Why is it important to reduce autocorrelation in a time series?

Reducing autocorrelation is important because it can lead to biased estimates and inaccurate predictions in time series analysis. Autocorrelated data violates the assumption of independent observations, which is necessary for many statistical tests and models to be valid.

3) How can autocorrelation be detected in a time series?

Autocorrelation can be detected visually by plotting the time series data and looking for patterns or trends. It can also be detected using statistical tests such as the autocorrelation function (ACF) or the Durbin-Watson test.

4) What are some methods for reducing autocorrelation in a time series?

Some methods for reducing autocorrelation in a time series include using differencing to remove trends and seasonality, using autoregressive (AR) or moving average (MA) models to account for autocorrelation, and incorporating exogenous variables into the analysis.

5) Are there any limitations to reducing autocorrelation in a time series?

Yes, there are limitations to reducing autocorrelation in a time series. It may not always be possible to completely eliminate autocorrelation, and some methods for reducing it may introduce other issues such as overfitting or underestimation of variability.

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