Smoothing Techniques
SMOOTHING TECHNIQUES
Several techniques are available to forecast time-series data that are stationary or that include no significant trend, cyclical, or seasonal effects. These techniques are often referred to as smoothing techniques because they produce forecasts based on “smoothing out” the irregular fluctuation effects in the time-series data. Three general categories of smoothing techniques are presented here:
• Naive forecasting models are simple models in which it is assumed that the more recent time periods of data represent the best predictions or forecasts for future outcomes. Naive models do not take into account data trend, cyclical effects, or seasonality. For this reason, naive models seem to work better with data that are reported on a daily or weekly basis or in situations that show no trend or seasonality. The simplest of the naive forecasting methods is the model in which the forecast for a given time period is the value for the previous time period.
Ft = x t-1
Where, Ft = the forecast value for time period t X t-1 = the value for time period t – 1
• Many naive model forecasts are based on the value of one time period. Often such forecasts become a function of irregular fluctuations of the data; as a result, the forecasts are “oversteered.” Using averaging models, a forecaster enters information from several time periods into the forecast and “smoothes” the data. Averaging models are computed by averaging data from several time periods and using the average as the forecast for the next time period. A moving average is an average that is updated or recomputed for every new time period being considered. The most recent information is utilized in each new moving average. This advantage is offset by the disadvantages that (1) it is difficult to choose
Several techniques are available to forecast time-series data that are stationary or that include no significant trend, cyclical, or seasonal effects. These techniques are often referred to as smoothing techniques because they produce forecasts based on “smoothing out” the irregular fluctuation effects in the time-series data. Three general categories of smoothing techniques are presented here:
• Naive forecasting models are simple models in which it is assumed that the more recent time periods of data represent the best predictions or forecasts for future outcomes. Naive models do not take into account data trend, cyclical effects, or seasonality. For this reason, naive models seem to work better with data that are reported on a daily or weekly basis or in situations that show no trend or seasonality. The simplest of the naive forecasting methods is the model in which the forecast for a given time period is the value for the previous time period.
Ft = x t-1
Where, Ft = the forecast value for time period t X t-1 = the value for time period t – 1
• Many naive model forecasts are based on the value of one time period. Often such forecasts become a function of irregular fluctuations of the data; as a result, the forecasts are “oversteered.” Using averaging models, a forecaster enters information from several time periods into the forecast and “smoothes” the data. Averaging models are computed by averaging data from several time periods and using the average as the forecast for the next time period. A moving average is an average that is updated or recomputed for every new time period being considered. The most recent information is utilized in each new moving average. This advantage is offset by the disadvantages that (1) it is difficult to choose