Exponential smoothing:

“Exponential smoothing methods have been

around since the 1950s, and are still the most popular forecasting methods used

in business and industry” (Rob J. Hyndman, 2008), which means that it’s

one of the most successful forecasting methods.

What is exponential smoothing?

Exponential smoothing (SE) is a model that

smooths the time series data using exponential purposes that gives weights reduced

over time(exponentially). It’s a popular method that creates a smoothed time

series. Exponential smoothing can be learned easily without a difficulty. It would

be better to be used if a new product has few sales data but then it will

provide a steady trend prediction of the future sales.

History of exponential smoothing:

There are a range of strategies that fall

under exponential smoothing, each one of these methods has the property of

forecasting a combination of weighted historical observations, with the latest

observations specified it will have a quite greater weight than observations at

the beginning. In addition, exponential smoothing gets to reflect that the

weights will get reduced exponentially when the observations become older.

In 1944 Robert G. Brown who at that time

was working for the US Navy as an operation research analyst, where he tracked

the speed and angle submarines firing using this method in a mechanical computing.

After that in the 1950s the method got extended to a discrete time series which

was a continuous time series at the beginning, it contained phrases to deal seasonality

and trend. At that time in 1956 presented one of his first applications

of forecasting the demand for spare parts the inventory system of the US Navy

in a meeting of the operations research society of America, then the methods

were further developed in 1963.

Subsequently, Charles

holt did work after that on a different exponential smoothing model than the

one Brown’s did with deference for the seasonality and trend way. His work was on

additive and multiplicative seasonal exponential smoothing in 1957. One of holt’s students Peter Winter wrote a

paper that provided empirical tests for his teacher method, the outcome of the

paper was called then Holt-Winters’ methods (or Winters). by 1960, Holt’s method

then become well recognized. In 1960 Holt did collaborate with John Muth which

he then introduced the first two long series of statistical models. Exponential

smoothing success in forecasting lead to several researchers to find out models

that can produce equivalent forecasts as these methods.