Describe smoothing techniques for forecasting models,including naive,simple average,moving average,weighted moving average,and exponential smoothing.

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The Answer is One group of time-series forecasting methods contains smoothing techniques.Among these techniques are naïve models,averaging techniques,and simple exponential smoothing.These techniques do much better if the time-series data are stationary or show no significant trend or seasonal effects.Naive forecasting models are models in which it is assumed that the more recent time periods of data represent the best predictions or forecasts for future outcomes.
Simple averages use the average value for some given length of previous time periods to forecast the value for the next period.
Moving averages are time period averages that are revised for each time period by including the most recent value(s)in the computation of the average and deleting the value or values that are farthest away from the present time period.A special case of the moving average is the weighted moving average,in which different weights are placed on the values from different time periods.
Simple (single)exponential smoothing is a technique in which data from previous time periods are weighted exponentially to forecast the value for the present time period.The forecaster can select how much to weight more recent values versus those of previous time periods.

Describe Smoothing Techniques for Forecasting Models,including Naive,simple Average,moving Average,weighted Moving