Forecasting involves the estimation of values we don’t know by using values that we know of. There are many forecasting methods, and exponential smoothing is just one of them. Exponential smoothing is a technique used to detect significant changes in data by considering the most recent data. Also known as averaging, this method is used in making short-term forecasts.
Given that there are many other ways to make forecasts, what makes exponential smoothing better in certain cases compared to others? Also, what makes it not ideal for certain scenarios?
List of Advantages of Exponential Smoothing
1. It is easy to learn and apply.
Only three pieces of data are required for exponential smoothing methods. One, it needs the forecast for the most recent time period. Two, it needs the actual value for that time period. And three, it needs the value of the smoothing constant, a weighting factor that reflects the weight given to the most recent data values.
2. It produces accurate forecasts.
An exponential smoothing method produces a forecast for one period ahead. Using the trend projection technique, forecasts for more periods ahead can then be generated. The forecast is considered accurate as it accounts for the difference between actual projections and what actually occurred.
3. It gives more significance to recent observations.
Observed data is the sum of two or more components, one being the random error which is the difference between the observed value and the true value. In a smoothing technique, the random variation is neglected. As such, it’s much more easier to see the underlying phenomenon.
List of Disadvantages of Exponential Smoothing
1. It produces forecasts that lag behind the actual trend.
The lag is a side effect of the smoothing process. There’s a reason this method has “smoothing” in its name because it neglects the ups and downs associated with random variation. As such, seeing this on a graph shows you a smoother line or curve. But ignoring the random variation also allows you to see the underlying phenomenon, which helps when presenting data and making a forecast of future values.
2. It cannot handle trends well.
Exponential smoothing is best used for forecasts that are short-term and in the absence of seasonal or cyclical variations. As a result, forecasts aren’t accurate when data with cyclical or seasonal variations are present. As such, this kind of averaging won’t work well if there is a trend in the series.
Methods like this are only accurate when a reasonable amount of continuity can between the past and future can be assumed. As such, it’s best suited for short-term forecasting as it assumes future patterns and trends will look like current patterns and trends. While this kind of assumption may sound reasonable in the short term, it creates problems the further the forecast goes.
That said, there are variations of exponential smoothing that can handle trend patterns. Holt’s method can calculate strong trend patterns while Winter’s method can cover a strong trend and seasonal pattern variations.