Time Series Analysis > Exponential Smoothing
What is Exponential Smoothing?
Exponential smoothing of time series data assigns exponentially decreasing weights for newest to oldest observations. In other words, the older the data, the less priority (“weight”) the data is given; newer data is seen as more relevant and is assigned more weight. Smoothing parameters (smoothing constants) — usually denoted by α — determine the weights for observations.
Exponential smoothing is usually used to make short term forecasts, as longer term forecasts using this technique can be quite unreliable.
- Simple (single) exponential smoothing uses a weighted moving average with exponentially decreasing weights.
- Holt’s trend-corrected double exponential smoothing is usually more reliable for handling data that shows trends, compared to the single procedure.
- Triple exponential smoothing (also called the Multiplicative Holt-Winters) is usually more reliable for parabolic trends or data that shows trends and seasonality..
1. Simple Exponential Smoothing
The basic formula is:
St = αyt-1 + (1 – α) St-1
- α = the smoothing constant, a value from 0 to 1. When α is close to zero, smoothing happens more slowly. Following this, the best value for α is the one that results in the smallest mean squared error (MSE). There are various ways you can do this, but a popular method is the Levenberg–Marquardt algorithm.
- t = time period.
- Ft-1 = forecast for the previous period,
- At-1 = Actual demand for the period,
- a = weight (must be between 0 and 1). The closer to zero, the smaller the weight.
Which formula to use is usually a moot point, as most exponential smoothing is performed using software. Whichever formula you use though, you’ll have to set an initial observation. This is a judgment call. You could use an average of the first few observations, or you could set the second smoothed value equal to the original observation value to get the ball rolling.
2. Double Exponential Smoothing
This method is deemed more reliable for analyzing data that shows a trend. In addition, this is a more complicated method which adds a second equation to the procedure:
bt = γ(St – St-1) + (1 – γ)bt-1
- γ is a constant that is chosen with reference to α. Like α it can be chosen through the Levenberg–Marquardt algorithm.
3. Triple Exponential Smoothing
If your data shows a trend and seasonality, use triple exponential smoothing. In addition to the equations for single and double smoothing, a third equation is used to handle the seasonality aspect:
It = Β yt/St + (1-Β)It-L+m
- y = observation,
- S = smoothed observation,
- b = trend factor,
- I = seasonal index,
- F = forecast m periods ahead,
- t = time period.
Like α and γ, the optimal Β minimizes the MSE.
Next: Exponential Smoothing in Excel > >
Roberts, S.W. (1959). “Control chart tests based on geometric moving averages.” Technometrics, I. 239-250.
If you prefer an online interactive environment to learn R and statistics, this free R Tutorial by Datacamp is a great way to get started. If you're are somewhat comfortable with R and are interested in going deeper into Statistics, try this Statistics with R track.Comments are now closed for this post. Need help or want to post a correction? Please post a comment on our Facebook page and I'll do my best to help!