Kütüphaneler

library(ggplot2)
library(forecast)
library(lmtest)
library(readxl)
library(tseries)
library(stats)
library(readxl)
Veri <- read_excel("ZamanSerisi_Veri.xlsx")
data=ts(Veri, start = 2014, frequency=12)

Zaman Serisi Grafiği

autoplot(data)

Bileşenlere Ayırma

autoplot(data)

Components <- decompose(data)
plot(Components)

plot(Components$trend)

Components$seasonal
##              Jan         Feb         Mar         Apr         May         Jun
## 2014 -0.93623167 -0.38258584 -0.25810667 -0.88102334 -1.36071084  0.40230999
## 2015 -0.93623167 -0.38258584 -0.25810667 -0.88102334 -1.36071084  0.40230999
## 2016 -0.93623167 -0.38258584 -0.25810667 -0.88102334 -1.36071084  0.40230999
## 2017 -0.93623167 -0.38258584 -0.25810667 -0.88102334 -1.36071084  0.40230999
## 2018 -0.93623167 -0.38258584 -0.25810667 -0.88102334 -1.36071084  0.40230999
## 2019 -0.93623167 -0.38258584 -0.25810667 -0.88102334 -1.36071084  0.40230999
## 2020 -0.93623167 -0.38258584 -0.25810667 -0.88102334 -1.36071084  0.40230999
## 2021 -0.93623167 -0.38258584 -0.25810667 -0.88102334 -1.36071084  0.40230999
## 2022 -0.93623167 -0.38258584 -0.25810667 -0.88102334 -1.36071084  0.40230999
## 2023 -0.93623167 -0.38258584                                                
##              Jul         Aug         Sep         Oct         Nov         Dec
## 2014  0.89704379  1.08037712  0.77783083  0.41845583  0.31637249 -0.07373167
## 2015  0.89704379  1.08037712  0.77783083  0.41845583  0.31637249 -0.07373167
## 2016  0.89704379  1.08037712  0.77783083  0.41845583  0.31637249 -0.07373167
## 2017  0.89704379  1.08037712  0.77783083  0.41845583  0.31637249 -0.07373167
## 2018  0.89704379  1.08037712  0.77783083  0.41845583  0.31637249 -0.07373167
## 2019  0.89704379  1.08037712  0.77783083  0.41845583  0.31637249 -0.07373167
## 2020  0.89704379  1.08037712  0.77783083  0.41845583  0.31637249 -0.07373167
## 2021  0.89704379  1.08037712  0.77783083  0.41845583  0.31637249 -0.07373167
## 2022  0.89704379  1.08037712  0.77783083  0.41845583  0.31637249 -0.07373167
## 2023

Üssel Düzeltme Yöntemi

HWData <- HoltWinters(data, gamma = FALSE)
HWData
## Holt-Winters exponential smoothing with trend and without seasonal component.
## 
## Call:
## HoltWinters(x = data, gamma = FALSE)
## 
## Smoothing parameters:
##  alpha: 0.4385627
##  beta : 0.02431247
##  gamma: FALSE
## 
## Coefficients:
##          [,1]
## a 44.17129385
## b  0.00184244
plot(HWData)

HWData$fitted
##              xhat    level         trend
## Mar 2014 47.40000 47.20000  0.2000000000
## Apr 2014 47.28554 47.09301  0.1925362195
## May 2014 47.43965 47.24803  0.1916241204
## Jun 2014 47.74823 47.55383  0.1944001052
## Jul 2014 48.23542 48.03407  0.2013496276
## Aug 2014 48.82516 48.61459  0.2105682303
## Sep 2014 48.39551 48.20014  0.1953723810
## Oct 2014 48.59290 48.39748  0.1954202409
## Nov 2014 46.68015 46.53477  0.1453819882
## Dec 2014 46.78953 46.64500  0.1445273710
## Jan 2015 46.93876 46.79412  0.1446390363
## Feb 2015 46.25738 46.13235  0.1250331852
## Mar 2015 46.31171 46.18836  0.1233551003
## Apr 2015 46.33996 46.21886  0.1210976816
## May 2015 46.84741 46.71714  0.1302678711
## Jun 2015 47.09115 46.95819  0.1329611167
## Jul 2015 47.49762 47.35817  0.1394530277
## Aug 2015 47.05415 46.92853  0.1256170896
## Sep 2015 48.05389 47.90753  0.1463648017
## Oct 2015 47.14283 47.02156  0.1212663270
## Nov 2015 47.73900 47.60646  0.1325384577
## Dec 2015 47.22511 47.10791  0.1171950228
## Jan 2016 46.79195 46.68782  0.1041322856
## Feb 2016 46.58524 46.48849  0.0967543165
## Mar 2016 46.46401 46.37243  0.0915804021
## Apr 2016 46.25730 46.17280  0.0845003303
## May 2016 46.40591 46.31988  0.0860218513
## Jun 2016 46.62404 46.53488  0.0891576414
## Jul 2016 47.10670 47.00820  0.0984975787
## Aug 2016 46.93265 46.84063  0.0920285898
## Sep 2016 47.54908 47.44461  0.1044754688
## Oct 2016 46.50845 46.43115  0.0772957521
## Nov 2016 46.58195 46.50474  0.0772056904
## Dec 2016 46.62234 46.54601  0.0763319174
## Jan 2017 46.10464 46.04241  0.0622324039
## Feb 2017 45.85033 45.79561  0.0547191092
## Mar 2017 45.79260 45.74055  0.0520499290
## Apr 2017 45.80305 45.75199  0.0510626212
## May 2017 45.94259 45.88942  0.0531626155
## Jun 2017 46.15631 46.09934  0.0569735450
## Jul 2017 45.60399 45.56148  0.0425118448
## Aug 2017 46.49824 46.43551  0.0627280897
## Sep 2017 46.51684 46.45516  0.0616806054
## Oct 2017 46.39126 46.33403  0.0572360734
## Nov 2017 46.45242 46.39509  0.0573292282
## Dec 2017 46.35144 46.29786  0.0535714902
## Jan 2018 46.02252 45.97803  0.0444930214
## Feb 2018 45.33814 45.31094  0.0271928233
## Mar 2018 45.30327 45.27756  0.0257199364
## Apr 2018 45.19276 45.17027  0.0224862541
## May 2018 45.30834 45.28365  0.0246960009
## Jun 2018 45.41914 45.39240  0.0267395674
## Jul 2018 44.89821 44.88447  0.0137404834
## Aug 2018 45.85613 45.81998  0.0361509260
## Sep 2018 45.73230 45.69994  0.0323537089
## Oct 2018 45.97475 45.93741  0.0373406171
## Nov 2018 45.88867 45.85426  0.0344110358
## Dec 2018 45.34418 45.32351  0.0206705524
## Jan 2019 45.03054 45.01781  0.0127357313
## Feb 2019 44.49049 44.49087 -0.0003850036
## Mar 2019 44.49438 44.49466 -0.0002835884
## Apr 2019 44.49662 44.49684 -0.0002236264
## May 2019 44.58776 44.58581  0.0019449317
## Jun 2019 44.68505 44.68084  0.0042079576
## Jul 2019 44.47136 44.47232 -0.0009638888
## Aug 2019 45.47156 45.44876  0.0227990800
## Sep 2019 44.87822 44.87004  0.0081747883
## Oct 2019 45.43525 45.41405  0.0212020881
## Nov 2019 45.44062 45.41979  0.0208262519
## Dec 2019 45.12874 45.11581  0.0129294021
## Jan 2020 45.08384 45.07228  0.0115567185
## Feb 2020 44.33897 44.34537 -0.0063972510
## Mar 2020 44.22522 44.23417 -0.0089452884
## Apr 2020 43.93541 43.95102 -0.0156117439
## May 2020 41.20854 41.28851 -0.0799645785
## Jun 2020 38.96846 39.09969 -0.1312358520
## Jul 2020 41.05260 41.13125 -0.0786530741
## Aug 2020 42.70229 42.73992 -0.0376299540
## Sep 2020 42.79840 42.83286 -0.0344556447
## Oct 2020 43.79788 43.80780 -0.0099147426
## Nov 2020 44.10338 44.10580 -0.0024283737
## Dec 2020 44.14435 44.14575 -0.0013981172
## Jan 2021 43.44919 43.46706 -0.0178648550
## Feb 2021 42.37601 42.41892 -0.0429132269
## Mar 2021 41.84973 41.90411 -0.0543862452
## Apr 2021 42.35699 42.39805 -0.0410551518
## May 2021 41.79619 41.84958 -0.0533916588
## Jun 2021 41.29528 41.35930 -0.0640135609
## Jul 2021 42.98537 43.00775 -0.0223793541
## Aug 2021 43.64340 43.64963 -0.0062295157
## Sep 2021 44.92043 44.89620  0.0242291161
## Oct 2021 45.29486 45.26232  0.0325413503
## Nov 2021 45.01525 44.99012  0.0251323774
## Dec 2021 45.03353 45.00856  0.0249697457
## Jan 2022 44.90867 44.88726  0.0214134342
## Feb 2022 44.47696 44.46631  0.0106584316
## Mar 2022 44.45305 44.44321  0.0098377974
## Apr 2022 44.39413 44.38593  0.0082059124
## May 2022 44.31513 44.30899  0.0061359610
## Jun 2022 43.05664 43.08052 -0.0238804797
## Jul 2022 44.13038 44.12821  0.0021719710
## Aug 2022 43.35522 43.37150 -0.0162782545
## Sep 2022 44.61689 44.60283  0.0140543334
## Oct 2022 44.75812 44.74105  0.0170730190
## Nov 2022 44.70416 44.68878  0.0153870169
## Dec 2022 44.58291 44.57077  0.0121438590
## Jan 2023 44.37812 44.37113  0.0069947815
## Feb 2023 44.30510 44.30000  0.0050955694
HWData$SSE
## [1] 261.7672
HWForecasts<-forecast(HWData, h=12)
HWForecasts
##          Point Forecast    Lo 80    Hi 80    Lo 95    Hi 95
## Mar 2023       44.17314 42.18094 46.16533 41.12633 47.21994
## Apr 2023       44.17498 41.99100 46.35896 40.83486 47.51509
## May 2023       44.17682 41.80845 46.54519 40.55471 47.79893
## Jun 2023       44.17866 41.63152 46.72581 40.28314 48.07419
## Jul 2023       44.18051 41.45892 46.90209 40.01821 48.34281
## Aug 2023       44.18235 41.28973 47.07497 39.75847 48.60623
## Sep 2023       44.18419 41.12322 47.24516 39.50284 48.86554
## Oct 2023       44.18603 40.95883 47.41324 39.25045 49.12162
## Nov 2023       44.18788 40.79612 47.57963 39.00063 49.37512
## Dec 2023       44.18972 40.63472 47.74471 38.75282 49.62662
## Jan 2024       44.19156 40.47435 47.90877 38.50658 49.87655
## Feb 2024       44.19340 40.31475 48.07205 38.26152 50.12529
plot(HWForecasts)