A Moving Average Process is a statistical model used to understand the pattern or trend of a time series data. It is a technique that helps to smooth out random fluctuations in data and identify underlying patterns or trends.
A Moving Average Process is calculated by taking the average of a specified number of data points over a time period, and moving the window of data points forward one at a time. This creates a new average for each point in time, resulting in a smooth line that represents the overall trend of the data.
The purpose of using a Moving Average Process is to identify and understand the underlying pattern or trend of a time series data. It helps to remove random fluctuations and anomalies, making it easier to see the overall trend and make predictions about future data points.
One limitation of a Moving Average Process is that it can only capture linear trends and may not be suitable for nonlinear or complex data patterns. Additionally, it can smooth out important data points and may not accurately represent sudden changes or outliers in the data.
A Moving Average Process takes into account a specified number of data points over a time period, while a Simple Average only considers the data points at a specific point in time. This means that a Moving Average Process is better at smoothing out fluctuations and identifying trends over time.