The moving average (MA) is a simple function for smoothing time series data. This function is based on averaging each observation of a series, when applicable, with its surrounding observations, that is, with a past, future, or a combination of both past and future observations, in chronological order. The output of this transformation process is a smoothed version of the original series. The MA function has a variety of applications, such as data smoothing, noise reduction, and trend estimation. Also, with some small modifications, this function can be used as a forecasting model. In Chapter 10, Forecasting with Exponential Smoothing Models, we will discuss the forecasting applications of the MA function in detail. The main components of the MA function are as follows:
- The rolling window: This is a generic function that slides across the series in...
