5.6 Summary: stationarity testing

The basic stationarity diagnostics are the following

• Plot your data. Look for
  • An increasing trend
  • A non-zero level (if no trend)
  • Strange shocks or steps in your data (indicating something dramatic changed like the data collection methodology)
• Apply stationarity tests
  • adf.test() p-value should be less than 0.05 (reject null)
  • kpss.test() p-value should be greater than 0.05 (do not reject null)
• If stationarity tests are failed, then try differencing to correct
  • Try ndiffs() in the forecast package or manually try different differences.