Forecasting methods

Forecasting Methods Time Series: Naive Forecast Forecasting helps in making ments of actual outcomes events that have not been observed. Different companies use different forecasting techniques to make such statements. Prediction is another term, which can mean the same as forecasting. Some companies and industries uses time series forecasting method, while others use estimating the average forecast. In time series: naive forecasting, an organization uses model to predict future values basing argument from the observed values. According to Hdyman (2009) naïve forecast technique is cost effective. In a stable data, the forecast for any period is equivalent to actual value of previous periods. Companies compile data to determine their current values and future position in business. Companies like Puma and Nike uses this technique to determine market progress and future sales volume. Communication and transport industries also use this technique to assess their progress (Turchin, 2010).
Estimating the Average Forecast
Estimating the average forecast is another techniques used by many organization. The value is calculated by determining forecast error. In determining forecast error, the actual value is subtracted from the predicted value of a time series. Forecast error can be either a calendar forecast or cross sectional forecast error. In calculation of forecast error, an organization can use calculating methods such as root mean squared error, mean percentage error, forecast bias and tracking signal (Kimberly, 2008). Some organization like Emirates Airline, Virgin Atlantic uses this technique. Use of forecast techniques has led to growth of many companies and industries. Each business organization should consider using forecast methods to determine progress.
References
Ellis Kimberly (2008). Production planning and inventory control. New York: McGraw Hill.
Turchin P (2010). Scientific prediction in historical sociology. London: Kogan Page publishers.
Steven Hdyman (2009). Forecasting Methods and Applications. New York: University of New York Press.