Multiplicative Winter’s Smoothing Method · Initializing the Holt-Winters method 1. Fit a 2×...
Transcript of Multiplicative Winter’s Smoothing Method · Initializing the Holt-Winters method 1. Fit a 2×...
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Multiplicative Winter’s Smoothing MethodLECTURE 6|TIME SERIES FORECASTING METHOD [email protected]. id
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Review What is the difference between additive and
multiplicative seasonal pattern in time series data?
What is the basic idea of additive Winter’s smoothing method?
What are the issues of additive Winter’s smoothing procedure?
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Seasonal Data
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Seasonal Data
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65.00
70.00
75.00
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58
Aditif
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40.00
50.00
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70.00
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90.00
100.00
110.00
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58
Multiplikatif
Additive Multiplicative
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Outline Time series data set with multiplicative seasonal
component
Winter’s smoothing method for multiplicative seasonal time series data
Ilustration
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EXPONENTIAL SMOOTHING FOR SEASONAL DATA Originally introduced by Holt (1957) and Winters (1960)
Generally known as Winters’ method
Basic idea:
seasonal adjustment linear trend model
Two types of adjustments are suggested: Additive
Multiplicative
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Aditive Model
level or linear trend component the seasonal adjustment
St = St+m = St+2m =… for t = 1,…, m − 1
length of the season (period) of the cycles
can in turn be represented by 𝛽0 + 𝛽1t
𝑌𝑡 = 𝐿𝑡 + 𝑆𝑡 + 휀𝑡
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Multiplicative Model
level or linear trend component the seasonal adjustment
St = St+m = St+2m =… for t = 1,…, m − 1
length of the season (period) of the cycles
can in turn be represented by 𝛽0 + 𝛽1t
𝑌𝑡 = 𝐿𝑡 𝑆𝑡 + 휀𝑡
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Additive Vs Multiplicative Holt-Winter’s Method
𝑌𝑡+ℎ 𝑡 = 𝐿𝑡 + 𝐵𝑡ℎ + 𝑆𝑡+ℎ−𝑚
Level
Trend
Seasonal
Additive:
𝑌𝑡+ℎ 𝑡 = 𝐿𝑡 + 𝐵𝑡ℎ 𝑆𝑡+ℎ−𝑚Multiplicative:
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Holt-Winters Multiplicative Formulation Suppose the time series is denoted by 𝑦1, … , 𝑦𝑛 with 𝑚
seasonal period.
𝐿𝑡 = 𝛼𝑌𝑡
𝑆𝑡−𝑚+ 1 − 𝛼 𝐿𝑡−1 + 𝐵𝑡−1
𝐵𝑡 = 𝛾 𝐿𝑡 − 𝐿𝑡−1 + 1 − 𝛾 𝐵𝑡−1
𝑆𝑡 = 𝛿𝑌𝑡
𝐿𝑡+ 1 − 𝛿 𝑆𝑡−𝑚
Estimate of the level:
Estimate of the trend:
Estimate of the seasonal factor:
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ℎ-step-ahead forecast Let 𝑌𝑡+ℎ 𝑡 be the ℎ-step forecast made using data to
time 𝑡
𝑌𝑡+ℎ 𝑡 = 𝐿𝑡 + 𝐵𝑡ℎ 𝑆𝑡+ℎ−𝑚
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Holt-Winters Additive Vs Multiplicative Formulation
Suppose the time series is denoted by 𝑦1, … , 𝑦𝑛 with 𝑚 seasonal period.
Additive Multiplicative
Est. of level
𝐿𝑡 = 𝛼 𝑌𝑡 − 𝑆𝑡−𝑚 + 1 − 𝛼 𝐿𝑡−1 + 𝐵𝑡−1 𝐿𝑡 = 𝛼𝑌𝑡
𝑆𝑡−𝑚+ 1 − 𝛼 𝐿𝑡−1 + 𝐵𝑡−1
Est. of trend
𝐵𝑡 = 𝛾 𝐿𝑡 − 𝐿𝑡−1 + 1 − 𝛾 𝐵𝑡−1 𝐵𝑡 = 𝛾 𝐿𝑡 − 𝐿𝑡−1 + 1 − 𝛾 𝐵𝑡−1
Est. of seasonal
𝑆𝑡 = 𝛿 𝑌𝑡 − 𝐿𝑡 + 1 − 𝛿 𝑆𝑡−𝑚 𝑆𝑡 = 𝛿𝑌𝑡
𝐿𝑡+ 1 − 𝛿 𝑆𝑡−𝑚
Forecast 𝑌𝑡+ℎ 𝑡 = 𝐿𝑡 + 𝐵𝑡ℎ + 𝑆𝑡+ℎ−𝑚 𝑌𝑡+ℎ 𝑡 = 𝐿𝑡 + 𝐵𝑡ℎ 𝑆𝑡+ℎ−𝑚
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The Procedure
Step 1: Initialize the value of 𝐿𝑡 , 𝐵𝑡, and 𝑆𝑡
Step 2: Update the estimate of 𝐿𝑡
Step 3: Update the estimate of 𝐵𝑡
Step 4: Update the estimate of 𝑆𝑡
Step 5: Conduct the ℎ-step-ahead forecast
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Initializing the Holt-Winters method
1. Fit a 2×𝑚 moving average smoother to the first 2 or 3 years of data.
2. Subtract smooth trend from the original data to get de-trended data. The initial seasonal values are then obtained from the averaged de-trended data.
3. Subtract the seasonal values from the original data to get seasonally adjusted data.
4. Fit a linear trend to the seasonally adjusted data to get the initial level 𝐿0 (the intercept) and the initial slope 𝐵0.
Hyndman (2010)
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Initializing the Holt-Winters method
Montgomery (2015):
Suppose a dataset consist of 𝑘 seasons.
𝐿0 = 𝑦𝑘− 𝑦1
𝑘−1 𝑚where 𝑦𝑖 =
1
𝑚 𝑡= 𝑖−1 𝑚+1
𝑖𝑚 𝑦𝑡
𝐵0 = 𝑦1 −𝑚
2 𝐿0
𝑆𝑗−𝑚 = 𝑚 𝑆𝑗∗
𝑖=1𝑚 𝑆𝑗
∗ , for 1 ≤ 𝑗 ≤ 𝑠, where 𝑆𝑗∗ =
1
𝑘 𝑡=1
𝑘 𝑦 𝑡−1 𝑚+𝑗
𝑦𝑡−𝑠+1
2−𝑗 𝛽0
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Initializing the Holt-Winters method
Used the basic principle of weighted moving average to give
more weight to more recent data and estimate the initial
values for the overall smoothing and the trend smoothing
components.
The initial values for the seasonal indices can be computed by calculating the average level for each observed season.
Hansun (2017)
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Slide 19
Procedures of Multiplicative Holt-Winters Method
Use the Sports Drink example as an illustration
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Slide 20
Procedures of Multiplicative Holt-Winters Method
0
50
100
150
200
250
0 5 10 15 20 25 30 35
Time
Sp
ort
s D
rin
k (y
)
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Slide 21
Procedures of Multiplicative Holt-Winters MethodObservations: ◦ Linear upward trend over the 8-year period
◦ Magnitude of the seasonal span increases as the level of the time series increases
Multiplicative Holt-Winters method can be applied to forecast future sales
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Slide 22
Procedures of Multiplicative Holt-Winters Method
Step 1: Obtain initial values for the level ℓ0, the growth rate b0, and the seasonal factors s-3, s-2, s-1, and s0, by fitting a least squares trend line to at least four or five years of the historical data. ◦ y-intercept = ℓ0; slope = b0
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Slide 23
Procedures of Multiplicative Holt-Winters Method
Example ◦ Fit a least squares trend line to the first 16 observations
◦ Trend line
◦ ℓ0 = 95.2500; b0 = 2.4706
ˆ 95.2500 2.4706ty t
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Slide 24
Procedures of Multiplicative Holt-Winters Method
Step 2: Find the initial seasonal factors1. Compute for the in-sample observations used for
fitting the regression. In this example, t = 1, 2, …, 16. ˆ
ty
1
2
16
ˆ 95.2500 2.4706(1) 97.7206
ˆ 95.2500 2.4706(2) 100.1912
......
ˆ 95.2500 2.4706(16) 134.7794
y
y
y
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Slide 25
Procedures of Multiplicative Holt-Winters Method
Step 2: Find the initial seasonal factors2. Detrend the data by computing for each time
period that is used in finding the least squares regression equation. In this example, t = 1, 2, …, 16.
ˆ/t t tS y y
1 1 1
2 2 2
16 16 16
ˆ/ 72 / 97.7206 0.7368
ˆ/ 116 /100.1912 1.1578
......
ˆ/ 120 /134.7794 0.8903
S y y
S y y
S y y
𝑆0,1
𝑆0,2
……
𝑆0,16
𝑆0,𝑡
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Slide 26
Procedures of Multiplicative Holt-Winters Method
Step 2: Find the initial seasonal factors3. Compute the average seasonal values for each of the k
seasons. The k averages are found by computing the average of the detrended values for the corresponding season. For example, for quarter 1,
1 5 9 13[1]
4
0.7368 0.7156 0.6894 0.68310.7062
4
S S S SS
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Slide 27
Procedures of Multiplicative Holt-Winters Method
Step 2: Find the initial seasonal factors4. Multiply the average seasonal values by the normalizing
constant
such that the average of the seasonal factors is 1. The initial seasonal factors are
[ ]( ) ( 1,2,..., )i L isn S CF i L 𝑆𝑖−𝑚
𝐶𝐹 =𝑚
𝑖=1𝑚 𝑆[𝑖]
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Slide 28
Procedures of Multiplicative Holt-Winters Method
Step 2: Find the initial seasonal factors4. Multiply the average seasonal values by the normalizing
constant such that the average of the seasonal factors is 1. ◦ Example
CF = 4/3.9999 = 1.0000
3 1 4 [1]
2 2 4 [2]
1 3 4 [3]
0 4 4 [1]
( ) 0.7062(1) 0.7062
( ) 1.1114(1) 1.1114
( ) 1.2937(1) 1.2937
( ) 0.8886(1) 0.8886
sn sn S CF
sn sn S CF
sn sn S CF
sn sn S CF
𝑆−3 = 𝑆1−4
𝑆−2 = 𝑆2−4
𝑆−1 = 𝑆3−4
𝑆0 = 𝑆4−4
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Slide 29
Procedures of Multiplicative Holt-Winters Method
Step 3: Calculate a point forecast of y1 from time 0 using the initial values
𝑦𝑡+ℎ 𝑡 = 𝐿𝑡 + 𝐵𝑡ℎ 𝑆𝑡+ℎ−𝑚
𝑦1 0 = 𝐿0 + 𝐵0 𝑆1−4 = 𝐿0 + 𝐵0 𝑆−3
𝑦1 0 = 95.25 + 2.4706 0.7062
𝑦1 0 = 69.0103
𝑡 = 1 , ℎ = 0
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Slide 30
Procedures of Multiplicative Holt-Winters Method
Step 4: Update the estimates ℓT, bT, and snT by using some predetermined values of smoothing constants.
Example: let = 0.2, = 0.1, and δ = 0.1
1 1 1 4 0 0( / ) (1 )( )
0.2(72 / 0.7062) 0.8(95.2500 2.4706) 98.5673
y sn b
l l
1 1 0 0( ) (1 )
0.1(98.5673 95.2500) 0.9(2.4706) 2.5553
b b
l l
2 1 1 2 4ˆ (1) ( )
(98.5673 2.5553)(1.1114) 112.3876
y b sn
l
1 1 1 1 4( / ) (1 )
0.1(72 / 98.5673) 0.9(0.7062) 0.7086
sn y sn
l
𝑠1−4
𝑠1−4
𝑠2−4
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Slide 31
053.135
2937.162031.27727.101
2ˆ
114239.1
1114.19.07727.1011161.0
1
62031.2
5553.29.05673.987727.1011.0
1
7727.101
5553.25673.988.01114.11162.0
1
43223
42222
1122
114222
snby
snysn
bb
bsny
𝑠2−4
𝑠2−4
𝑠3−4
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Slide 32
945.77
7086.065212.23464.107
4ˆ
889170.0
8886.09.03464.107961.0
1
65212.2
6349.29.05393.1043464.1071.0
1
3464.107
6349.25393.1048.08886.0962.0
1
45445
44444
3344
334444
snby
snysn
bb
bsny
𝑠4−4
𝑠4−4
𝑠5−4
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Slide 33
…… ……
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Slide 34
Procedures of Multiplicative Holt-Winters Method
Step 5: Find the most suitable combination of , , and δ that minimizes SSE (or MSE)
Example: Use Solver in Excel as an illustration
SSE
alpha
gamma
delta
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Slide 35
…… ……
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Slide 36
Multiplicative Holt-Winters Method
p-step-ahead forecast made at time T
Example
33 32 32 33 4ˆ (32) ( ) (168.1213 2.3028)(0.7044) 120.0467y b sn l
34 32 32 34 4ˆ (32) ( 2 ) [168.1213 2(2.3028)](1.1038) 190.6560y b sn l
35 32 32 35 4ˆ (32) ( 3 ) [(168.1213 3(2.3028)](1.2934) 226.3834y b sn l
36 32 32 36 4ˆ (32) ( 4 ) [(168.1213 4(2.3028)](0.8908) 157.9678y b sn l
𝑦𝑡+ℎ 𝑡 = 𝐿𝑡 + 𝐵𝑡ℎ 𝑆𝑡+ℎ−𝑚 ℎ = 1, 2, 3, … .
𝑠33−4
𝑠34−4
𝑠35−4
𝑠36−4
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Slide 37
Multiplicative Holt-Winters Method
Example
Forecast Plot for Sports Drink Sales
0
50
100
150
200
250
0 5 10 15 20 25 30 35 40
Time
Fo
recasts
Observed values
Forecasts
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Chapter Summary Additive Vs multiplicative Holt-Winter’s
smoothing ?
Basic idea of multiplicative Holt-Winter’s smoothing?
Procedure in multiplicative Holt-Winter’s smoothing?
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Another Example
See example 4.8 on Montgomery (2015) , chapter 4 page 309.
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Exercise1. Montgomery (2015) exercise 4.27 , 4.28, 4.29
2. Montgomery (2015) exercise 4.30 part (c)
3. Montgomery (2015) exercise 4.32 part (c)
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Next Topic…
Regression for Time Series Data Set (part 1)
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ReferencesHyndman, R.J. 2010. Initializing the Holt-Winters method.
https://robjhyndman.com/hyndsight/hw-initialization/ [March 7th,2018]
Hyndman, R.J and Athanasopoulos, G. 2013. Forecasting: principles andpractice. https://www.otexts.org/ fpp/6/2/ [March 7th, 2018]
Montgomery, D.C., Jennings, C.L., Kulahci, M. 2015. Introduction to TimeSeries Analysis and Forecasting, 2nd ed. New Jersey: John Wiley &Sons.
Hansun, S. 2017. New Estimation Rules for Unknown Parameters onHolt-Winters Multiplicative Method. J.Math. Fund. Sci. , Vol. 49 (2):127-135. DOI: 10.5614/j.math.fund.sci.2017.49.2.3.
Wan, A. 2017. Exponential Smoothing. http://personal.cb.cityu.edu.hk/msawan/teaching/ms6215/Exponential%20Smoothing%20Methods.ppt [March 7th, 2018]
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The handouts are available on the following site:
stat.ipb.ac.id/en
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