movingaveragedecompositie
Moving Average Decomposition is a statistical technique used to analyze time series data by breaking it down into several components. This method helps in understanding the underlying patterns and trends within the data. The primary components identified through moving average decomposition are:
1. Trend: This represents the long-term movement or direction in the data. It is often smoothed out
2. Seasonal: This component accounts for regular patterns or cycles that repeat over a fixed period. For
3. Residual: This is the remainder of the data after the trend and seasonal components have been
The moving average decomposition process typically involves the following steps:
1. Calculate the moving average of the time series data to smooth out short-term fluctuations and highlight
2. Subtract the moving average from the original data to obtain the seasonal component.
3. Further decompose the seasonal component to isolate the trend and seasonal effects.
4. Subtract the trend and seasonal components from the original data to obtain the residual component.
Moving average decomposition is widely used in various fields such as economics, finance, and engineering to