Question

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Moving averages, weighted moving averages, and exponential smoothing methods are three forecasting methods that are appropriate for a time series with a horizontal pattern. These methods are easy to use and generally provide a high level of accuracy for short-range forecasts, such as a forecast for the next time period. “The moving averages method uses the average of the most recent k data values in the time series as the forecast for the next period (Anderson, Sweeney, Williams, Camm, Cochran, Fry, & Ohlmann, p.211). “Weighted moving averages involve selecting a different weight for each data value in the moving average and then computing a weighted average of the most recent k values as the forecast”(Anderson, Sweeney, Williams, Camm, Cochran, Fry, & Ohlmann, P. 214). “Exponential smoothing also uses a weighted average of past time series values as a forecast; it is a special case of the weighted moving averages method in which we select only one weight—the weight for the most recent observation. The weights for the other data values are computed automatically and become smaller as the observations move farther into the past”(Anderson, Sweeney, Williams, Camm, Cochran, Fry, & Ohlmann, p. 215).

There are several measures of forecast accuracy. These measures are used to determine how well a particular forecasting method is able to reproduce the time series data that are already available. The key concept associated with measuring forecast accuracy is forecast error. “The mean absolute error, denoted MAE, is a measure of forecast accuracy that avoids the problem of positive and negative forecast errors offsetting one another”(Anderson, Sweeney, Williams, Camm, Cochran, Fry, & Ohlmann, p. 207). “Another measure that avoids the problem of positive and negative errors offsetting each other is obtained by computing the average of the squared forecast errors” and is referred to as the mean squared error (Anderson, Sweeney, Williams, Camm, Cochran, Fry, & Ohlmann, p. 208). These measures of forecast accuracy simply measure how well the forecasting method is able to forecast historical values of the time series. Measures of forecast accuracy are important factors in comparing different forecasting methods. Obviously the better the forecast accuracy is, the better the prediction will be and the organization can plan accordingly. A forecast is not beneficial if it is not accurate and these methods and measures allow us to get the best possible forecast.

Answer 1

Forecasting

This paragraph explains about the three forecasting measures, are Moving averages, weighted moving averages, and exponential smoothing method used in time series data with horizontal patterns which provides high level of accuracy for short-range forecasts, such as a forecast for the next time period and determine how well a particular forecasting method is able to reproduce the time series data that are already available. In these methods, the moving averages method uses the average of the most recent k data values in the time series as the forecast for the next period. “Weighted moving averages involve selecting a different weight for each data value in the moving average and then computing a weighted average of the most recent k values as the forecast”. “Exponential smoothing also uses a weighted average of past time series values as a forecast; it is a special case of the weighted moving averages method in which we select only one weight—the weight for the most recent observation. The weights for the other data values are computed automatically and become smaller as the observations move farther into the past”.

The theme of a research article focuses on several measures of forecast accuracy. The research makes an important point of “The mean absolute error, denoted MAE, is a measure of forecast accuracy that avoids the problem of positive and negative forecast errors offsetting one another”. “Another measure that avoids the problem of positive and negative errors offsetting each other is obtained by computing the average of the squared forecast errors” and is referred to as the mean squared error.

The authors conclude that these measures of forecast accuracy simply measure how well the forecasting method is able to forecast historical values of the time series. Measures of forecast accuracy are important factors in comparing different forecasting methods. Obviously the better the forecast accuracy is, the better the prediction will be and the organization can plan accordingly. A forecast is not beneficial if it is not accurate and these methods and measures allow us to get the best possible forecast.