Lissage par moyenne mobile
Moyenne Mobile Smoothing is a statistical technique commonly used in analyse de séries temporelles to reduce noise and reveal underlying trends in data. It involves calculating the average of a set number of data points over a specified period, known as the ‘window size’ or ‘lag’. As nouvelles données becomes available, the oldest data point is dropped, and a new data point is added, creating a ‘moving’ average.
Il existe plusieurs types de moyennes mobiles, notamment :
- Moyenne Mobile Simple (SMA) : This is the most basic form, where the average is computed by adding the values within the window and dividing by the number of points.
- Moyenne Mobile Pondérée (WMA) : In this method, more recent data points are given more weight que les plus anciens, ce qui peut rendre la moyenne plus réactive aux changements récents.
- Moyenne Mobile Exponentielle (EMA) : This approach applies a smoothing factor to give exponentially decreasing weights to older data points, allowing for a quicker response to recent price movements.
Moving Average Smoothing is widely used in various fields such as finance for analyzing stock prices, in economics for forecasting trends, and in traitement du signal to filter out noise. It helps analysts and decision-makers identify patterns and make informed decisions based on clearer data presentations.
Dans l'ensemble, bien que les moyennes mobiles puissent lisser efficacement les fluctuations, elles peuvent aussi avoir un décalage par rapport aux changements en temps réel, ce qui est une considération importante lors de l'interprétation des résultats.