orclYahoo <- read.csv("/cloud/project/orclYahoo.csv")
lnstock=log(orclYahoo$Adj.Close[1:252])
lnstock
## [1] 3.961534 3.972325 3.946077 3.972325 3.948543 3.947975 3.958162 3.949301
## [9] 3.956471 3.966200 3.982268 3.974358 3.972695 3.968246 3.970287 3.966386
## [17] 3.968431 3.975497 3.972156 3.985270 3.987652 3.986920 3.984903 3.985637
## [25] 3.987469 3.993311 3.997669 3.994584 4.001829 3.995856 4.000384 3.992218
## [33] 3.987103 3.990577 3.991671 3.976053 3.975868 3.980672 3.988018 3.965441
## [41] 3.978457 3.986370 3.987835 3.985270 3.969737 3.979196 3.979750 3.958869
## [49] 3.953012 3.933493 3.932333 3.932913 3.911020 3.903881 3.938697 3.940234
## [57] 3.949595 3.962254 3.976238 3.974941 3.968991 3.973086 3.962630 3.959811
## [65] 3.955472 3.951305 4.029945 4.014561 4.025548 4.013670 4.024137 4.026077
## [73] 4.029594 4.047684 4.055412 4.062231 4.069341 4.073549 4.074725 4.083910
## [81] 4.081247 4.078242 4.073549 4.062014 4.051379 4.053618 4.043589 4.044631
## [89] 4.042024 4.053446 4.056539 4.060135 4.052586 4.042720 4.021803 4.014315
## [97] 4.013599 3.975638 3.979352 3.967795 3.992058 3.978239 3.966106 3.979537
## [105] 3.948487 3.953635 3.972471 3.977682 3.961023 3.965919 3.964979 3.934243
## [113] 3.944849 3.949824 3.941390 3.945425 3.943506 3.940235 3.960834 3.977125
## [121] 3.964415 3.990043 4.006951 4.021626 3.978054 3.975452 3.964039 3.958945
## [129] 3.953064 3.968357 3.970229 3.980463 3.967983 3.977125 3.979352 3.981758
## [137] 3.998987 3.976382 3.964227 3.977497 4.000984 3.994799 3.981018 3.994310
## [145] 4.011762 4.036678 4.029091 4.027141 4.018765 4.003982 3.994677 4.005253
## [153] 3.986578 3.986947 3.989346 3.987686 3.996325 3.999249 4.000161 3.993576
## [161] 4.002892 4.011041 4.015000 4.015539 4.020553 4.029623 4.028560 4.031391
## [169] 4.024654 4.022516 4.028383 4.024298 4.025720 4.025187 4.025009 4.027851
## [177] 4.030861 4.029976 4.031745 4.023407 4.010680 3.995410 3.992107 3.997057
## [185] 3.999796 4.008513 4.017691 4.026431 4.029268 3.993943 3.984358 3.962827
## [193] 3.973182 3.970369 3.977108 3.979158 3.974118 3.976548 3.974493 3.960174
## [201] 3.965473 3.983617 3.980088 3.985283 3.987502 3.991389 3.995996 3.997283
## [209] 3.999668 4.005149 4.002960 4.015121 4.009694 4.011325 4.002229 4.006969
## [217] 3.990280 3.964426 3.978934 3.974058 3.976124 3.959860 3.971989 3.989725
## [225] 4.002595 4.002046 4.002229 4.007151 4.010057 4.020519 4.012954 4.015842
## [233] 4.015482 4.015301 4.016563 4.001498 3.963666 3.931041 3.952205 3.927503
## [241] 3.901164 3.929863 3.888754 3.922765 3.871201 3.857989 3.828859 3.883212
## [249] 3.800421 3.683867 3.869742 3.754667
acf(lnstock, lag.max = 20)
pacf(lnstock, lag.max = 20)
difflnstock=diff(lnstock,1)
difflnstock
## [1] 0.0107907357 -0.0262481445 0.0262481445 -0.0237815769 -0.0005686177
## [6] 0.0101868349 -0.0088604216 0.0071697389 0.0097288239 0.0160683611
## [11] -0.0079096558 -0.0016634217 -0.0044493725 0.0020417338 -0.0039014110
## [16] 0.0020455115 0.0070658752 -0.0033407906 0.0131131247 0.0023824848
## [21] -0.0007325207 -0.0020170403 0.0007340177 0.0018324680 0.0058415334
## [26] 0.0043588787 -0.0030856581 0.0072452090 -0.0059733316 0.0045285820
## [31] -0.0081663380 -0.0051150708 0.0034737773 0.0010945257 -0.0156181708
## [36] -0.0001851363 0.0048040813 0.0073462061 -0.0225768548 0.0130162219
## [41] 0.0079124818 0.0014651985 -0.0025655451 -0.0155328601 0.0094593560
## [46] 0.0005537043 -0.0208806910 -0.0058573971 -0.0195184510 -0.0011600513
## [51] 0.0005802624 -0.0218930287 -0.0071399580 0.0348166411 0.0015366854
## [56] 0.0093610862 0.0126595365 0.0139835575 -0.0012968097 -0.0059502287
## [61] 0.0040945815 -0.0104556175 -0.0028192701 -0.0043384399 -0.0041674722
## [66] 0.0786400318 -0.0153836952 0.0109873118 -0.0118785917 0.0104675884
## [71] 0.0019395632 0.0035167885 0.0180905214 0.0077274117 0.0068189510
## [76] 0.0071102399 0.0042084056 0.0011752268 0.0091858229 -0.0026634874
## [81] -0.0030050208 -0.0046925415 -0.0115352642 -0.0106346995 0.0022391134
## [86] -0.0100294307 0.0010422467 -0.0026075948 0.0114227840 0.0030927746
## [91] 0.0035962005 -0.0075497583 -0.0098659140 -0.0209165512 -0.0074879432
## [96] -0.0007161496 -0.0379608384 0.0037134175 -0.0115565354 0.0242627158
## [101] -0.0138186986 -0.0121327253 0.0134306145 -0.0310496411 0.0051472423
## [106] 0.0188364569 0.0052112261 -0.0166592010 0.0048955761 -0.0009396340
## [111] -0.0307363418 0.0106065891 0.0049750670 -0.0084339936 0.0040341982
## [116] -0.0019189830 -0.0032708805 0.0205996624 0.0162909354 -0.0127104577
## [121] 0.0256281195 0.0169080356 0.0146745396 -0.0435719980 -0.0026012513
## [126] -0.0114137017 -0.0050939086 -0.0058806789 0.0152934175 0.0018720000
## [131] 0.0102336096 -0.0124804040 0.0091426784 0.0022263504 0.0024062899
## [136] 0.0172291621 -0.0226050221 -0.0121553390 0.0132699989 0.0234873081
## [141] -0.0061852053 -0.0137806860 0.0132916768 0.0174518706 0.0249166617
## [146] -0.0075871288 -0.0019501724 -0.0083756650 -0.0147830645 -0.0093057867
## [151] 0.0105763413 -0.0186750190 0.0003695051 0.0023986922 -0.0016600172
## [156] 0.0086389789 0.0029239605 0.0009119848 -0.0065849380 0.0093159859
## [161] 0.0081485300 0.0039596141 0.0005388338 0.0050142790 0.0090692002
## [166] -0.0010626972 0.0028313415 -0.0067376480 -0.0021370755 0.0058660787
## [171] -0.0040849042 0.0014227553 -0.0005332491 -0.0001778487 0.0028414157
## [176] 0.0030102428 -0.0008844713 0.0017680900 -0.0083371611 -0.0127275759
## [181] -0.0152702349 -0.0033027670 0.0049500414 0.0027395087 0.0087162713
## [186] 0.0091784311 0.0087399531 0.0028373845 -0.0353253955 -0.0095852765
## [191] -0.0215303831 0.0103549681 -0.0028134444 0.0067390233 0.0020501255
## [196] -0.0050396458 0.0024297008 -0.0020554887 -0.0143182129 0.0052989477
## [201] 0.0181432152 -0.0035280320 0.0051948872 0.0022181295 0.0038870756
## [206] 0.0046078783 0.0012864102 0.0023846842 0.0054814497 -0.0021890377
## [211] 0.0121609995 -0.0054269127 0.0016311739 -0.0090959330 0.0047401911
## [216] -0.0166899793 -0.0258538382 0.0145080268 -0.0048752769 0.0020655344
## [221] -0.0162635957 0.0121282327 0.0177359450 0.0128702086 -0.0005482504
## [226] 0.0001828018 0.0049220953 0.0029053951 0.0104618196 -0.0075648595
## [231] 0.0028886100 -0.0003606203 -0.0001803950 0.0012618118 -0.0150651368
## [236] -0.0378317353 -0.0326252849 0.0211637842 -0.0247022193 -0.0263383147
## [241] 0.0286987210 -0.0411086253 0.0340107842 -0.0515641108 -0.0132119151
## [246] -0.0291303751 0.0543532440 -0.0827911135 -0.1165539640 0.1858747260
## [251] -0.1150743951
adf.test(lnstock)
##
## Augmented Dickey-Fuller Test
##
## data: lnstock
## Dickey-Fuller = -0.47865, Lag order = 6, p-value = 0.9823
## alternative hypothesis: stationary
adf.test(difflnstock)
## Warning in adf.test(difflnstock): p-value smaller than printed p-value
##
## Augmented Dickey-Fuller Test
##
## data: difflnstock
## Dickey-Fuller = -5.4296, Lag order = 6, p-value = 0.01
## alternative hypothesis: stationary
pricearima <- ts(lnstock,start = c(2019,09),frequency =252)
fitlnstock<-auto.arima(pricearima)
fitlnstock
## Series: pricearima
## ARIMA(2,0,0) with non-zero mean
##
## Coefficients:
## ar1 ar2 mean
## 0.5564 0.4125 3.9574
## s.e. 0.0616 0.0621 0.0382
##
## sigma^2 estimated as 0.0004025: log likelihood=627.65
## AIC=-1247.3 AICc=-1247.14 BIC=-1233.18
plot(pricearima,type = 'l')
title('ORCL PRICE')
exp(lnstock)
## [1] 52.53787 53.10786 51.73201 53.10786 51.85977 51.83029 52.36097 51.89908
## [9] 52.27252 52.78355 53.63855 53.21596 53.12751 52.89166 52.99976 52.79338
## [17] 52.90149 53.27660 53.09892 53.79979 53.92812 53.88863 53.78005 53.81954
## [25] 53.91825 54.23414 54.47105 54.30323 54.69810 54.37234 54.61913 54.17491
## [33] 53.89851 54.08607 54.14530 53.30622 53.29635 53.55300 53.94786 52.74354
## [41] 53.43455 53.85902 53.93800 53.79979 52.97058 53.47403 53.50365 52.39804
## [49] 52.09202 51.08512 51.02589 51.05551 49.94990 49.59453 51.35165 51.43063
## [57] 51.91433 52.57572 53.31608 53.24699 52.93109 53.14827 52.59547 52.44740
## [65] 52.22035 52.00317 56.25780 55.39898 56.01102 55.34962 55.93204 56.04063
## [73] 56.23806 57.26469 57.70892 58.10377 58.51838 58.76517 58.83427 59.37720
## [81] 59.21926 59.04157 58.76517 58.09119 57.47668 57.60552 57.03066 57.09013
## [89] 56.94146 57.59562 57.77402 57.98216 57.54606 56.98111 55.80164 55.38536
## [97] 55.34571 53.28412 53.48235 52.86784 54.16624 53.42288 52.77863 53.49226
## [105] 51.85687 52.12448 53.11562 53.39314 52.51102 52.76873 52.71917 51.12342
## [113] 51.66855 51.92625 51.49014 51.69828 51.59917 51.43067 52.50111 53.36341
## [121] 52.68943 54.05721 54.97899 55.79173 53.41297 53.27421 52.66961 52.40200
## [129] 52.09474 52.89757 52.99669 53.54182 52.87775 53.36341 53.48235 53.61120
## [137] 54.54288 53.32377 52.67952 53.38324 54.65191 54.31492 53.57155 54.28836
## [145] 55.24411 56.63790 56.20981 56.10030 55.63238 54.81601 54.30827 54.88570
## [153] 53.87022 53.89013 54.01955 53.92995 54.39787 54.55716 54.60694 54.24854
## [161] 54.75628 55.20428 55.42331 55.45318 55.73193 56.23968 56.17994 56.33923
## [169] 55.96092 55.84145 56.16998 55.94100 56.02065 55.99078 55.98083 56.14012
## [177] 56.30937 56.25959 56.35915 55.89122 55.18437 54.34810 54.16889 54.43769
## [185] 54.58703 55.06491 55.57264 56.06047 56.21977 54.26845 53.75076 52.60585
## [193] 53.15341 53.00408 53.36248 53.47199 53.20319 53.33262 53.22310 52.46647
## [201] 52.74523 53.71093 53.52177 53.80053 53.92000 54.13000 54.38000 54.45000
## [209] 54.58000 54.88000 54.76000 55.43000 55.13000 55.22000 54.72000 54.98000
## [217] 54.07000 52.69000 53.46000 53.20000 53.31000 52.45000 53.09000 54.04000
## [225] 54.74000 54.71000 54.72000 54.99000 55.15000 55.73000 55.31000 55.47000
## [233] 55.45000 55.44000 55.51000 54.68000 52.65000 50.96000 52.05000 50.78000
## [241] 49.46000 50.90000 48.85000 50.54000 48.00000 47.37000 46.01000 48.58000
## [249] 44.72000 39.80000 47.93000 42.72000
forecastedvalues_ln = forecast(fitlnstock,h=10)
forecastedvalues_ln
## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## 2020.032 3.808430 3.782718 3.834142 3.769107 3.847753
## 2020.036 3.790875 3.761450 3.820299 3.745874 3.835875
## 2020.040 3.803285 3.768492 3.838078 3.750073 3.856496
## 2020.044 3.802948 3.764554 3.841342 3.744230 3.861666
## 2020.048 3.807880 3.766015 3.849746 3.743852 3.871908
## 2020.052 3.810486 3.765672 3.855300 3.741949 3.879022
## 2020.056 3.813970 3.766459 3.861481 3.741309 3.886632
## 2020.060 3.816984 3.767047 3.866921 3.740612 3.893356
## 2020.063 3.820098 3.767939 3.872258 3.740327 3.899870
## 2020.067 3.823075 3.768876 3.877273 3.740185 3.905965
plot(forecastedvalues_ln)
