MGT 530 Discussion

The difference between long-term and short-term forecasting is basically the time duration and the response in terms. This implies that short-term fashion forecasting, for instance, covers the future for a period of up to two to three years, and it is also immediate to primarily use. Short-term forecasts represent programs, personal agendas, delivery, and production establishment. On the contrary, long-forecasting covers the future for very long periods of time, usually more than three to four years (Carol, 2019). It relies on the system of the earth’s flexibility and variability. There are basically four techniques of forecasting: simple linear regression, moving average, straight line, and multiple linear regression.

The simple linear technique of forecasting is used when making comparisons between a single dependent variable and its other independent counterpart. The technique requires an individual to have statistical knowledge and concepts. The data used in the technique is a sample of various observations made of a given phenomenon of interest. Compared to other machine learning algorithms, they have relatively lower time complexities (Cheepati & Prasad, 2016). Mathematical equations used in linear regression are relatively easier to comprehend and make interpretations. Some of the challenges encountered by linear regression include proxy variables, multicollinearity, specification, measurement error, and limited dependent variables.

Moving average is normally applicable for repeated forecasts, and it involves historical data and minimum levels of mathematical concepts. The technique is used for forecasting goods or products with constant demands, and it is useful when the separation of random variations is needed (Cheepati & Prasad, 2016). The disadvantage of the technique is that the moving averages require extensive post-data records and that the averages remain within the past levels on most occasions.

The straight-line technique is used to measure constant rates of growth, and it requires historical data and involves fewer mathematical concepts. The technique is considered the easiest because it requires no complicated mathematical concepts but only basic math skills (Cheepati & Prasad, 2016). However, it is considered a poor technique because the historical data that it majorly relies on is not in a straight line.

Multiple linear regression is a forecasting technique used while comparing and contrasting more than one dependent variable with its independent counterparts. The technique requires the use of statistical concepts and skills. The data used in the technique is a sample of various observations made of a given phenomenon of interest, thus allowing an account for critical factors in a single model, thus provides precise and more accurate outcomes (Singh et al., 2012). The disadvantages of multiple linear regression include proxy variables, multicollinearity, specification, measurement error, and limited dependent variables.

Example: My uncle’s grocery shop records an annual rate of growth of 4% over the past three years, and the shop is to continue for the next two or more years. By calculating the growth rate of the new year at 4% over the current year and the subsequent years at 5 percent above that of the next year, the shop is liable for making more accurate and precise predictions about the number of people that will be required at the shop, whether more workers need to be hired, and the cost of payrolls for every one of those years. This is an explicit example of the straight-line technique of forecasting. My rationale for this choice of technique is because of its simplicity, and it does not require extensive and complex mathematical equations as other techniques require but still provides the same information.