Why exponential smoothing

In this case, a damping coefficient phi is used to determine and control the dampening rate, i. Additive Dampening: Make a damped trend linea r. TES explicitly adds support to the univariate time series for seasonality; it is also referred to as Holt-Winters Exponential Smoothing on the name of two contributors Charles Holt and Peter Winters. The Holt-Winters exponential smoothing model permits the level, trend and seasonality patterns to change over time as it is an adaptive method.

In correspondence with the trend, seasonality can be modeled in the particular of additive or multiplicative process for the linear and exponential variation in the seasonality. Multiplicative Seasonality: TES with an exponential seasonality. Being an advanced variation of exponential smoothing, TES can construct single and double exponential smoothing models through configuration. In addition to that, in order to make and ensure the accuracy of seasonality models, one should specify the number of time steps in a seasonal period.

These methods are the family of traditional forecasting algorithms that work efficiently when time series data exhibit a clear and seasonal pattern. Exponential smoothing has the elements as Error, Trend, and season that can be either additively or multiplicatively. In order to find the trend, season or error, time series decomposition is an accurate approach as it makes the plot of each component as a distinct subplot. As the trends reflect upward or downward behavior tendency , applied as additively, and if varies exponentially, it is multiplicative.

Similarly, if the magnitude of seasonal trends changes linearly, it is additive, and if varies exponentially, it is multiplicatively. Knowing what smoothing constant to use is an important part of demand planning.

You need to know your company, know your products, and know your inventory. That method is trial and error. I would suggest creating 4 columns of possible constants, next to your original. You can then view them on a graph and determine which constant, or which combination of constants best fits the graph.

Forecasting using a combination of quantitative methods and your own experience as a demand planner is what will set you apart in your industry. This can be thought of as a weighted average where all of the weight is given to the last observation. Hence, the average method assumes that all observations are of equal importance, and gives them equal weights when generating forecasts.

We often want something between these two extremes. For example, it may be sensible to attach larger weights to more recent observations than to observations from the distant past. This is exactly the concept behind simple exponential smoothing.

We present two equivalent forms of simple exponential smoothing, each of which leads to the forecast Equation 7. Recall that fitted values are simply one-step forecasts of the training data. So, the weighted average form leads to the same forecast Equation 7. An alternative representation is the component form.

They then start to envision a complicated mathematical calculation that likely requires a degree in mathematics to understand, and hope there is a built-in Excel function available if they ever need to do it. The reality of exponential smoothing is far less dramatic and far less traumatic. The truth is, exponential smoothing is a very simple calculation that accomplishes a rather simple task.

It just has a complicated name because what technically happens as a result of this simple calculation is actually a little complicated. Smoothing is a very common statistical process. Any time you use an average to describe something, you are using a smoothed number. If you think about why you use an average to describe something, you will quickly understand the concept of smoothing.

For example, we just experienced the warmest winter on record. How are we able to quantify this? In demand forecasting, we use smoothing to remove random variation noise from our historical demand. This allows us to better identify demand patterns primarily trend and seasonality and demand levels that can be used to estimate future demand.

Not surprisingly, the most common way people remove noise from demand history is to use a simple average—or more specifically, a moving average. A moving average just uses a predefined number of periods to calculate the average, and those periods move as time passes. The controlling input of the exponential smoothing calculation is known as the smoothing factor also called the smoothing constant.