<p>$$\text{EWMA}_t = \alpha \cdot \text{observation}_t + (1 - \alpha) \cdot \text{EWMA}_{t-1}$$</p><p>Exponentially Weighted Moving Average (EWMA) is a technique used to calculate a smoothed average of a series of values over time. It assigns exponentially decreasing weights to older observations, giving more importance to recent values. It is computed by taking a weighted average of the current value and the previous moving average. The weights decrease exponentially as you go further back in time. This helps capture trends and smooth out noise in the data. The rate at which the weights decay is controlled by a parameter called the smoothing factor. Higher smoothing factors place more emphasis on recent observations, while lower values give more weight to historical data.</p>


$$\text{EWMA}_t = \alpha \cdot \text{observation}_t + (1 - \alpha) \cdot \text{EWMA}_{t-1}$$

Exponentially Weighted Moving Average (EWMA) is a technique used to calculate a smoothed average of a series of values over time. It assigns exponentially decreasing weights to older observations, giving more importance to recent values. It is computed by taking a weighted average of the current value and the previous moving average. The weights decrease exponentially as you go further back in time. This helps capture trends and smooth out noise in the data. The rate at which the weights decay is controlled by a parameter called the smoothing factor. Higher smoothing factors place more emphasis on recent observations, while lower values give more weight to historical data.