Dickey Fuller Test Sine Wave or seasonallity

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SUMMARY

The discussion centers on the application of the Dickey-Fuller test in analyzing sine wave data for stationarity. The user employed the MATLAB command [hd, pValue] = adftest(sin(0:0.1:300)); and received a test statistic indicating stationarity, allowing them to reject the unit root hypothesis. However, questions arose regarding the necessity of accounting for seasonality in pseudo oscillatory data and the appropriate number of lags to include in the test. Clarification on these points is essential for accurate interpretation of the test results.

PREREQUISITES
  • Understanding of the Dickey-Fuller test and its application in time series analysis
  • Familiarity with MATLAB and its statistical functions
  • Knowledge of stationarity and non-stationarity concepts in time series data
  • Basic grasp of seasonality and its impact on time series analysis
NEXT STEPS
  • Research the impact of seasonality on the Dickey-Fuller test results
  • Learn about preprocessing techniques for time series data to remove seasonality
  • Explore the concept of lag selection in time series analysis
  • Investigate alternative tests for stationarity, such as the KPSS test
USEFUL FOR

Data analysts, statisticians, and researchers working with time series data, particularly those interested in testing for stationarity and understanding the implications of seasonality in their analyses.

EmmaSaunders1
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Hi can someone please clarify info regarding the application and validity of the Dickey Fuller test.

If I perform the test using a sine wave would I be required to somehow take into account the seasonality. From my understanding a sine wave is non stationary, using the MATLAB command below I receive a value for hd of 1 and therefore can reject the unit root hypothesis and thus the signal is stationary, does this make sense? I have searched for answers to no avail.

[hd pValue]=adftest(sin(0:0.1:300));

In general I have some pseudo oscillatory data and do not know if I need to pre-process the data to remove seasonality before performing this test? If someone could enlighten me on the amount of lags I am required to keep that would also be great.

Thanks for any help
 
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