Weighted Moving Average (WMA) is a type of moving average that gives more weight to the recent data points in a series and less weight to the older data points. Unlike the simple moving average, which assigns equal weight to each data point, WMA assigns different weights to different data points based on their position in the series.The WMA calculation is done by multiplying each data point by a weight, which is determined by its position in the series. The weighted data points are then summed and divided by the total weight. The weight for each data point is typically calculated based on a specific formula, such as the exponential smoothing formula or the time-series analysis formula.WMA is commonly used in technical analysis and financial modeling to smooth out short-term fluctuations in data and to identify trends. It is especially useful for financial markets where the most recent data is considered to be the most relevant. The WMA can also be used to forecast future values based on past data.It's important to note that the choice of weights in the WMA calculation can significantly impact the results, and choosing the right weighting scheme is essential to get the most accurate results.
