TD_SMOOTHMA applies a smoothing function to series producing a result series that highlights the trend associated with the series. You can then proceed to model and forecast based on the smoothed series or subtract the trend series from the original to potentially produce a stationary series. The stationary series can be modeled and forecasted. After forecasting the modified series, you must forecast the trend series and add that back to the forecasted series to generate the final result.
Time series data smoothing reduces noise and uncovers underlying patterns or trends in a time series dataset. It involves mathematical or statistical techniques to remove short-term fluctuations, such as random noise, outliers, or seasonal variation, and expose the long-term behavior of the data.
One method of time series smoothing is moving average, which involves computing the average of a set of neighboring values over a specified window size. Another method is exponential smoothing, which applies a weighted moving average to the data with exponentially decreasing weights as the data gets further away from the present time.
The goal of time series smoothing is to provide a clearer and more concise representation of the data in order to identify trends and make accurate predictions. It is used in finance, economics, and weather forecasting.