Comparisons and Contrasts of Exponential Smoothing Methods

Running head: 620 DB3 1
620 DB3
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620 DB3 2
Introduction
The analysis involves the comparisons and contrasts of the exponential smoothing
methods. The exponential methods include; double exponential smoothing, single exponential
smoothing and triple exponential smoothing methods. Exponential smoothing methods are the
rules of the thumb technique used for smoothing time series. Smoothing time series is achieved
by the use of window function in the exponential (Koehler, 2016). It is a way in which data are
smoothened to make forecasts and presentations. The methods are mostly applied in economics
and finance fields. The analysis also involves determinations of the commonalities and the
differences that exist between the seasonal and non-seasonal data series. Non-seasonal time
series is the trend and irregular component which involves attempts for the separation of the time
series. On the other hand, the seasonal time series includes constant in size of the time. Some
examples will be discussed in this analysis based on how one model would be fit as compared to
another and reason.
Comparisons of the exponential smoothing methods
All the exponential smoothing methods give the different levels of weights regarding the
most recent observations. Besides, all these methods assign the weights. Also, these methods
produce better forecasting for the exchange rates (Gardner 2015). They all offer the rates which
narrow the range of one point to another within a specified time series. They cannot produce the
right prediction for the more extended period forecasting period. The methods revise the
forecasts, especially when there are new observations available.
Contrasts between the exponential smoothing methods