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| Property | Value |
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| URL | https://www.itl.nist.gov/div898/handbook/pmc/section4/pmc433.htm |
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| Meta Title | 6.4.3.3. Double Exponential Smoothing |
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| Meta Canonical | null |
| Boilerpipe Text | 6.
Process or Product Monitoring and Control
6.4.
Introduction to Time Series Analysis
6.4.3.
What is Exponential Smoothing?
Double Exponential Smoothing
Double exponential smoothing uses two constants and is better
at handling trends
As was
previously
observed
, Single Smoothing does not excel in following the data when
there is a trend. This situation can be improved by the introduction
of a second equation with a second constant,
γ
,
which must be chosen in conjunction with
α
.
Here are the two equations associated with Double Exponential Smoothing.
S
t
=
α
y
t
+
(
1
−
α
)
(
S
t
−
1
+
b
t
−
1
)
0
≤
α
≤
1
b
t
=
γ
(
S
t
−
S
t
−
1
)
+
(
1
−
γ
)
b
t
−
1
0
≤
γ
≤
1
Note that the current value of the series is used to calculate its
smoothed value replacement in double exponential smoothing.
Initial Values
Several methods to choose the initial values
As in the case for single smoothing, there are a variety of schemes
to set initial values for
S
t
and
b
t
in double smoothing.
S
1
is in general set to
y
1
.
Here are three suggestions for
b
1
.
b
1
=
y
2
−
y
1
b
1
=
1
3
[
(
y
2
−
y
1
)
+
(
y
3
−
y
2
)
+
(
y
4
−
y
3
)
]
b
1
=
y
n
−
y
1
n
−
1
Comments
Meaning of the smoothing equations
The first smoothing equation adjusts
S
t
directly for the trend of the previous period,
b
t
−
1
,
by adding it to the last smoothed value,
S
t
−
1
.
This helps to eliminate the lag and brings
S
t
to the appropriate base of the current value.
The second smoothing equation then updates the trend, which is
expressed as the difference between the last two values. The equation
is similar to the basic form of single smoothing, but here applied
to the updating of the trend.
Non-linear optimization techniques can be used
The values for
α
and
γ
can be obtained via non-linear optimization techniques, such as the
Marquardt Algorithm. |
| Markdown | 
| | |
|---|---|
| | |
| 6\.4.3.3. | Double Exponential Smoothing |
| *Double exponential smoothing uses two constants and is better at handling trends* | As was [previously observed](https://www.itl.nist.gov/div898/handbook/pmc/section4/pmc432.htm#Single%20Exponential%20Smoothing%20with), Single Smoothing does not excel in following the data when there is a trend. This situation can be improved by the introduction of a second equation with a second constant, γ, which must be chosen in conjunction with α. Here are the two equations associated with Double Exponential Smoothing. S t \= α y t \+ ( 1 − α ) ( S t − 1 \+ b t − 1 ) 0 ≤ α ≤ 1 b t \= γ ( S t − S t − 1 ) \+ ( 1 − γ ) b t − 1 0 ≤ γ ≤ 1 Note that the current value of the series is used to calculate its smoothed value replacement in double exponential smoothing. |
| | **Initial Values** |
| *Several methods to choose the initial values* | As in the case for single smoothing, there are a variety of schemes to set initial values for S t and b t in double smoothing. S 1 is in general set to y 1. Here are three suggestions for b 1. b 1 \= y 2 − y 1 b 1 \= 1 3 \[ ( y 2 − y 1 ) \+ ( y 3 − y 2 ) \+ ( y 4 − y 3 ) \] b 1 \= y n − y 1 n − 1 |
| | **Comments** |
| *Meaning of the smoothing equations* | The first smoothing equation adjusts S t directly for the trend of the previous period, b t − 1, by adding it to the last smoothed value, S t − 1. This helps to eliminate the lag and brings S t to the appropriate base of the current value. The second smoothing equation then updates the trend, which is expressed as the difference between the last two values. The equation is similar to the basic form of single smoothing, but here applied to the updating of the trend. |
| *Non-linear optimization techniques can be used* | The values for α and γ can be obtained via non-linear optimization techniques, such as the Marquardt Algorithm. |
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| Shard | 183 (laksa) |
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| Unparsed URL | gov,nist!itl,www,/div898/handbook/pmc/section4/pmc433.htm s443 |