Statilising
Statilising is a term used in statistics, data science, and engineering to describe the act of making a system, dataset, or model more stable over time. It typically aims to reduce volatility, sensitivity to noise, or changes in conditions so that outputs or inferences remain reliable and interpretable.
The term is a neologism without a universally adopted definition. In different disciplines it may refer to
Techniques commonly associated with statilising include:
- Data smoothing and filtering (for example, moving average or exponential smoothing)
- Detrending and normalization to remove systematic shifts
- Variance-stabilizing transforms (such as Box-Cox transformations)
- Regularization and robust modeling to limit sensitivity to outliers
- State estimation and Kalman filtering to produce stable estimates from noisy data
- Sensor fusion or data fusion to reduce measurement noise
- Experimental design aimed at reducing inherent variation
Applications span several fields, including financial time-series analysis, engineering and process control, climate and environmental modelling,
Related concepts include stabilisation, smoothing, variance stabilization, stationary processes, and regularization. The term remains a topic