How do you handle non-stationary time-series data? | by NS Academy | Medium
1. Understand non-stationarity A time series is non-stationary when its mean, variance, or autocorrelation changes over time. This can happen due to: 2. Detect it First, visualize the series — if you see a slope, seasonal cycles, or varying spread, it’s likely non-stationary. Then, confirm with statistical tests: Join Medium for free to get updates from this writer. Remember me for faster sign in 3. Transform or preprocess 4. Choose modeling approach Two ways to handle it: 5. Validate properly Always use rolling-window or time-series cross-validation to respect temporal order. After transformation or modeling, check the residuals — they should look like white noise: no autocorrelation, constant variance, and mean around zero. Help Status About Careers Press Blog Store Privacy Rules Terms Text to speech