attach(y)
head(y,10)
## # A tibble: 10 x 4
## Start.Date freq day weekdays
## <date> <int> <int> <fctr>
## 1 2014-01-01 4257 1 3
## 2 2014-01-02 15595 2 4
## 3 2014-01-03 11527 3 5
## 4 2014-01-04 6826 4 6
## 5 2014-01-05 9003 5 7
## 6 2014-01-06 12942 6 1
## 7 2014-01-07 17267 7 2
## 8 2014-01-08 19553 8 3
## 9 2014-01-09 18969 9 4
## 10 2014-01-10 18593 10 5
#########################################################
####################################################
################################################################
modelt<- ts(y$freq,frequency = 365,start = c(2014,1))
plot.ts(modelt)
modelt
## Time Series:
## Start = c(2014, 1)
## End = c(2017, 1)
## Frequency = 365
## [1] 4257 15595 11527 6826 9003 12942 17267 19553 18969 18593 15394
## [12] 11789 17541 20528 19036 18697 17255 15070 15803 20996 21512 20828
## [23] 19844 19042 14329 6965 18130 19729 10483 16839 13662 13724 15617
## [34] 20224 20998 28928 24927 19564 10736 11519 19085 18356 17844 20747
## [45] 12672 11041 18171 18816 19267 21284 19209 21027 19663 13504 21250
## [56] 20482 23700 20445 13608 18477 11539 17961 22811 24848 24412 24106
## [67] 25103 33340 24109 23628 26878 27234 27781 29139 33881 26191 25276
## [78] 26459 23478 23591 18833 17279 23825 19234 20284 23238 23879 28970
## [89] 26186 26491 29476 28598 28015 27092 25302 16150 18696 26388 29343
## [100] 28982 28188 25339 30975 28469 29675 30680 27388 21819 21082 7062
## [111] 20219 22468 26830 28799 20158 24781 21037 29189 45802 48418 16450
## [122] 26238 30985 32493 33273 32004 28544 19161 29808 19040 17420 25937
## [133] 25105 33788 34822 35809 37568 42948 36748 33323 34132 30539 30192
## [144] 18423 32905 11919 14280 21785 28911 30984 34518 36142 33179 29081
## [155] 25594 34235 37044 29543 43852 34007 39759 39217 39639 39604 32289
## [166] 28546 31325 37374 35105 35805 36494 41756 41016 34878 37665 39227
## [177] 35088 35533 19890 26338 31852 39086 40425 42664 37847 26905 30721
## [188] 35756 34711 38422 31811 27206 37737 32790 38002 40412 41845 41917
## [199] 39935 36416 33843 38046 42410 42021 41982 31458 43080 39295 35834
## [210] 41874 42566 41441 39078 37084 41044 39049 39768 37792 41552 33375
## [221] 46375 23331 30946 33898 36903 26429 31489 46505 39247 30389 34954
## [232] 36415 32845 33572 29539 30440 6078 18217 31872 31481 31980 30340
## [243] 32795 27124 36538 36130 35077 34547 32861 33739 36686 37345 36743
## [254] 34957 36714 33675 31366 36276 37152 38162 38039 36317 30543 32893
## [265] 34847 36124 32339 34990 35034 34545 37353 31353 36991 32402 36594
## [276] 37438 18676 32674 24117 31147 23248 26319 32020 30050 22718 13131
## [287] 28081 25044 31077 32251 28415 30003 31288 24435 30295 30914 28737
## [298] 29562 26152 33334 34934 23011 32054 35927 31390 15691 22117 23255
## [309] 27604 27932 26489 17163 23739 27973 28666 24634 27519 21159 22457
## [320] 17529 21156 25785 28459 28719 23743 17941 5960 25587 24135 22733
## [331] 26387 26401 23814 19703 24752 21366 25058 19840 22703 17220 12649
## [342] 23290 23723 24793 22827 20225 17995 14283 22761 22885 21916 22994
## [353] 21767 14550 11692 18377 16435 10287 38139 7545 6277 9180 12291
## [364] 12970 12612 9433 15125 5714 9234 20372 20613 22332 15601 22104
## [375] 14709 14575 17199 24697 23565 22968 22663 15268 13199 23034 23247
## [386] 22782 23278 21892 17539 15094 22710 25000 21536 21275 21171 10250
## [397] 11305 21640 19869 21850 21200 20955 13149 15778 23858 24211 23680
## [408] 23227 17699 14423 16952 16381 25064 25132 16898 20715 16198 9086
## [419] 21764 23688 21592 20036 24367 13529 15641 22258 23845 24485 24896
## [430] 25337 27365 20017 24037 27674 27419 28131 25700 18101 10662 19824
## [441] 25780 26439 24077 25226 16116 18618 25592 24083 25672 20305 26348
## [452] 18986 8905 22106 22766 23539 22682 11298 13633 19369 25439 29441
## [463] 30643 31520 32052 25113 29204 28048 34519 37470 30489 29796 29595
## [474] 19736 32057 34908 32381 32773 32102 25194 19427 30489 31783 26030
## [485] 31621 29579 22231 21165 26795 26488 24438 31144 26878 27810 32226
## [496] 33719 33820 36699 15609 32343 32526 31323 24288 28331 30534 34833
## [507] 33435 23849 30018 29502 34809 36407 34387 26023 29206 14998 27864
## [518] 27939 35377 39451 34803 32820 35407 32587 33255 35707 38808 35172
## [529] 30298 25235 34558 37923 38591 38687 35966 26295 30393 29557 37472
## [540] 38940 38673 37019 36581 25706 39082 43326 42641 38262 39611 38283
## [551] 29716 38620 37908 41703 72504 42779 42091 23240 27416 35348 34574
## [562] 39306 38505 38491 36693 31878 40696 37915 39092 16034 32608 12398
## [573] 31904 36101 36761 36145 36675 43017 43295 35402 37644 38770 63468
## [584] 39486 38993 40441 35335 34318 36548 25974 25419 33739 30616 35695
## [595] 34530 31640 32554 35882 44187 27001 17400 28152 23738 30528 32950
## [606] 22618 24972 9639 25556 30180 30950 29709 22317 29429 31692 32781
## [617] 33116 35734 34426 28848 26166 25811 28066 18653 32075 29829 33492
## [628] 31141 23676 20434 33977 29396 34783 32636 29817 33138 34436 34941
## [639] 34562 35503 28956 29884 20741 25892 28629 33676 34123 29416 27796
## [650] 31292 31545 30579 30874 30075 24327 23070 29952 32718 18922 30326
## [661] 30273 16876 23410 30364 32436 23881 27339 23970 27464 22848 28033
## [672] 30710 23462 24503 25431 14881 18754 28474 29206 29899 28962 25252
## [683] 8898 17594 26507 23649 26445 23695 25788 12751 13118 23908 21175
## [694] 26000 26626 23517 16336 11392 21766 26559 27789 25899 25956 16511
## [705] 15478 25734 23430 27562 23115 23259 16657 12291 23835 20201 25558
## [716] 25804 25730 18754 13060 18136 17458 18451 8908 22423 10230 9378
## [727] 13732 15592 12301 13889 9797 7195 4869 20533 22805 23038 18083
## [738] 20762 11583 14320 21092 22799 24586 22186 22446 16091 11762 22762
## [749] 23819 23800 23378 18328 17218 16360 25724 20224 21708 25324 22526
## [760] 16381 11019 23606 24514 24436 25154 22597 13126 14101 19852 23308
## [771] 23626 25186 22819 9869 13697 21789 24386 19034 23517 22173 11136
## [782] 14670 20463 25657 25481 24174 23580 14745 13451 23585 20704 20635
## [793] 24416 23970 12215 12510 22672 23129 19142 23504 20893 21573 21687
## [804] 25448 25224 25322 27060 23047 17903 17043 25852 29150 26837 19329
## [815] 25236 11109 10028 10216 18814 26516 28304 27428 24786 25335 25389
## [826] 30927 24244 22410 27494 20013 23781 24262 31065 32108 30924 15936
## [837] 17827 27288 28512 33719 34577 31937 20532 22060 18873 24389 27862
## [848] 28432 28901 25659 25039 29747 19724 32296 36567 39154 40131 44561
## [859] 45495 30875 21705 27971 38585 35115 30450 34821 37469 36810 24347
## [870] 34687 36105 26794 31139 35480 36580 31979 37539 36133 31307 31252
## [881] 24069 16227 27188 29189 29587 30151 40909 39582 35137 34621 41675
## [892] 40146 29027 18135 27601 30218 37742 31435 32024 29698 31412 22382
## [903] 39003 36659 25201 36535 26367 29634 33752 31701 31556 35934 28111
## [914] 30811 34264 37683 40955 42931 39673 37316 34530 29592 32800 30186
## [925] 36163 40408 37047 38933 38387 43711 46625 45220 41793 36406 42134
## [936] 39123 41050 42528 33761 39068 35744 46258 46358 30107 28949 40735
## [947] 39527 40579 42586 38671 38194 40054 38949 38069 40646 43801 44654
## [958] 41821 40899 42986 39475 25867 24734 28660 36093 42559 41140 38249
## [969] 38228 32673 26338 34725 39993 39453 40353 34669 23812 27936 28246
## [980] 38031 40323 41034 36574 17326 39997 39787 44084 44999 42658 22216
## [991] 27563 29863 33449 37660 40696 38448 38970 36256 30067 35084 36094
## [1002] 38899 34863 34148 17836 27363 35903 37163 36342 35359 33365 27243
## [1013] 25423 33069 35256 32592 30832 32644 23780 18725 29027 33065 33997
## [1024] 32644 30844 26330 23834 30375 33314 34364 34232 33781 28129 24183
## [1035] 34776 32334 32369 31186 22145 19649 13576 17330 26383 18897 26101
## [1046] 28095 9712 19295 24842 28812 27723 27742 26896 18305 10728 19423
## [1057] 27724 29118 28215 27697 19819 15655 27022 27848 27165 26477 26229
## [1068] 18124 15468 26478 28347 29760 28662 30084 15063 17741 25528 26765
## [1079] 29158 28480 27931 17483 15189 23754 25120 23343 21682 14534 7978
## [1090] 37401 11637 11003 12546 14228 11687 11590
modelt1<- auto.arima(modelt)
plot.ts(modelt1$residuals)
qqnorm(modelt1$residuals)
y1<- re2017
pf<- forecast(modelt1,h=10)
plot(pf)
summary(pf)
##
## Forecast method: ARIMA(5,1,2)
##
## Model Information:
## Series: modelt
## ARIMA(5,1,2)
##
## Coefficients:
## ar1 ar2 ar3 ar4 ar5 ma1 ma2
## 0.6148 -0.6395 -0.2463 -0.2016 -0.1639 -1.1414 0.8381
## s.e. 0.0608 0.0416 0.0396 0.0370 0.0343 0.0516 0.0557
##
## sigma^2 estimated as 30339351: log likelihood=-10983.42
## AIC=21982.83 AICc=21982.96 BIC=22022.82
##
## Error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set 13.94085 5487.977 3988.984 -4.908538 18.17164 0.661106
## ACF1
## Training set -0.00806169
##
## Forecasts:
## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## 2017.0027 12039.71 4980.775 19098.65 1243.9997 22835.42
## 2017.0055 13298.55 5488.476 21108.62 1354.0742 25243.02
## 2017.0082 14045.30 5857.362 22233.23 1522.9318 26567.66
## 2017.0110 14024.75 5447.850 22601.65 907.5131 27141.99
## 2017.0137 13149.75 4167.049 22132.46 -588.1073 26887.61
## 2017.0164 12113.51 2792.189 21434.82 -2142.2188 26369.23
## 2017.0192 11684.11 1870.567 21497.66 -3324.4098 26692.64
## 2017.0219 12180.03 1714.088 22645.97 -3826.2466 28186.30
## 2017.0247 13194.52 2119.673 24269.36 -3742.9949 30132.03
## 2017.0274 13959.21 2452.439 25465.98 -3638.8782 31557.30
y1$fp<- pf$fitted[1:339]
head(re2017,10)
## # A tibble: 10 x 5
## X1 Start.Date freq day weekdays
## <int> <date> <int> <int> <int>
## 1 1 2017-01-01 6536 1 7
## 2 2 2017-01-02 11979 2 1
## 3 3 2017-01-03 19622 3 2
## 4 4 2017-01-04 22144 4 3
## 5 5 2017-01-05 23594 5 4
## 6 6 2017-01-06 18975 6 5
## 7 7 2017-01-07 13881 7 6
## 8 8 2017-01-08 13538 8 7
## 9 9 2017-01-09 38091 9 1
## 10 10 2017-01-10 28106 10 2
ggplot(data=y1,aes(x=y1$Start.Date,y=freq))+geom_line()+
geom_line(data=y1,aes(x=Start.Date,y=fp),col="red")
mean((re2017$freq-pf$fitted[1:339])^2)
## [1] 67258748