How To Check If A Time Series Data Set Is Stationary
How to check if a fourth dimension series is stationary in R?
To cheque if a time series is stationary, we can use Dickey-Fuller exam using adf.test office of tseries parcel. For instance, if we have a time series object say TimeData so to check whether this time serial is stationary or not we tin can use the control adf.test(TimeData).
Example1
Alive Demo
x1<-ts(rpois(240,5),frequency=12) x1
Output
Jan Feb Mar Apr May Jun Jul Aug Sep Oct November December 1 3 7 vii 1 1 vi five 4 viii 5 2 3 2 five 0 3 4 iv 3 2 4 six v 5 4 3 5 8 six 2 3 9 5 4 7 4 4 vii 4 2 five 4 five 7 9 9 2 3 2 7 7 5 3 5 iii 3 8 2 2 3 5 four 3 seven 6 6 vi 5 4 vi 6 5 six 4 two 7 5 7 5 2 3 vii 3 vii 7 iii 7 4 5 6 viii ii 5 vi 4 half-dozen 5 six 3 five 4 nine three 9 4 four 3 seven 8 5 1 seven 3 5 iv 7 ten 4 four 7 ix 6 6 8 4 four four iii 3 11 5 2 5 1 2 half dozen 2 ii 5 iv 3 half-dozen 12 4 vii 9 2 8 5 half dozen three three 5 4 ii 13 vii 12 2 iii 7 iii 1 7 6 8 6 4 14 4 4 6 ten viii 9 4 ii 7 3 6 9 fifteen 5 five 6 11 2 2 6 v 8 x 6 9 sixteen 5 four 2 three 5 14 4 5 three ix vii 11 17 four 5 6 half-dozen 4 10 5 6 three 7 7 three 18 viii 7 4 ix iv 6 1 2 2 vii 5 viii 19 8 6 5 2 3 8 8 4 v 3 three eight xx 3 4 4 2 4 2 8 three 5 6 iv four
Loading tseries package and testing for stationarity of x1 −
library(tseries) adf.test(x1)
Augmented Dickey-Fuller Test data: x1 Dickey-Fuller = -v.3203, Lag guild = 6, p-value = 0.01 alternative hypothesis: stationary Warning bulletin: In adf.examination(x1) : p-value smaller than printed p-value
Example2
Live Demo
x2<-ts(rnorm(80),frequency=4) x2
Output
Qtr1 Qtr2 Qtr3 Qtr4 1 0.60770751 1.51860885 0.42749703 0.68703506 2 0.42218841 0.32858411 0.17599648 0.87994947 3 -1.33761406 0.31770494 -0.47925768 0.97272742 4 -0.37615238 -0.22445651 0.84534205 1.62197957 5 -0.89668820 one.73200007 -0.08857733 1.00659180 half-dozen 0.43669956 -one.73037915 -0.20687863 -1.00296218 vii 0.63507344 -1.19167403 0.10682158 -0.35904410 viii -0.23533581 1.73153525 0.30313272 0.79616948 9 0.14177702 ane.05396871 0.10664840 one.00615408 10 -0.75120735 one.40710169 1.03382332 0.84761125 11 -0.09219708 -2.90114437 0.86964912 0.75207072 12 -1.98357706 0.92872611 -0.59287936 1.10598932 xiii -0.24146436 -0.44565952 -0.63415612 -0.28752626 fourteen -one.98998974 -0.53391601 ane.14723658 -i.17779097 15 -0.55899287 0.91836259 0.17393828 2.37323800 sixteen -0.73209001 0.18996601 -0.60507321 one.48610650 17 -1.38941874 -0.12928990 0.66282081 0.76798462 xviii 0.27651434 0.36685046 1.35956161 -1.53075985 19 -2.67204336 0.21251039 -1.75949873 -0.33758449 20 1.20913418 0.68987712 two.28974722 ane.37072301
Testing for stationarity of x2 −
adf.test(x2)
Augmented Dickey-Fuller Test data: x2 Dickey-Fuller = -iii.6685, Lag society = four, p-value = 0.03288 alternative hypothesis: stationary
Published on 06-Mar-2021 11:54:18
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How To Check If A Time Series Data Set Is Stationary,
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