Differencing in Time Series

Why Do Differencing in Time Series?

Differencing is a technique used to make a non-stationary time series stationary (Stationary Time Series) by removing trends and, if needed, seasonal effects.

• First-order differencing:
  • Subtracts each observation from its previous value.
  • Captures the change from one time step to the next.
  • Helps remove linear trends in the series.
• Higher-order differencing:
  • If first-order differencing is not enough, you can apply second-order differencing:
  • Captures changes in the rate of change, useful for stronger trends.
• Seasonal differencing:
  • If seasonal patterns remain after first-order differencing, perform seasonal differencing
• Why it matters:
  • Differencing stabilizes the mean of the series.
  • Makes the series suitable for models like ARIMA and SARIMA, which assume stationarity.
• Next steps:
  • After differencing, check stationarity with statistical tests such as ADF Test and KPSS Test.
  • Visual inspection and ACF Plots can also confirm whether trends or seasonality have been removed.