Types of stationarity
When it comes to identifying if the data is stationary, it means identifying the fine-grained notions of stationarity in the data. The types of stationarity observed in time series data include
- Trend Stationary – A time series that does not show a trend.
- Seasonal Stationary – A time series that does not show seasonal changes.
- Strictly Stationary – The joint distribution of observations is invariant to time shift.
How to Check if Time Series Data is Stationary with Python?
Time series data are generally characterized by their temporal nature. This temporal nature adds a trend or seasonality to the data that makes it compatible for time series analysis and forecasting. Time-series data is said to be stationary if it doesn’t change with time or if they don’t have a temporal structure. So, it is highly necessary to check if the data is stationary. In time series forecasting, we cannot derive valuable insights from data if it is stationary.
Example plot of stationary data: