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How to decompose a time series with trend and seasonal components using loess method in R?
To decompose a time series with trend and seasonal components using loess method in R, we can follow the below steps −
First of all, create a time series object.
Then, use stl function to decompose the time series with trend and seasonal components.
Example
Create the time series object
Let’s create a time series object using ts function −
x<-sample(1:1000,48) TimeSeries<-ts(x,start=c(2017,1),end=c(2020,12),frequency=12) TimeSeries
Output
On executing, the above script generates the below output(this output will vary on your system due to randomization) −
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2017 3 686 888 748 632 170 922 395 293 460 244 454 2018 540 560 496 436 619 936 286 243 495 580 542 14 2019 171 983 923 422 698 302 838 858 398 45 8 927 2020 700 323 743 201 588 654 197 620 199 929 954 213
Decompose the time series
Using stl function to decompose the time series object TimeSeries with trend and seasonal components as shown below −
x<-sample(1:1000,48) TimeSeries<-ts(x,start=c(2017,1),end=c(2020,12),frequency=12) stl(TimeSeries,s.window="periodic")
Output
Call:
stl(x = TimeSeries, s.window = "periodic")
Components
seasonal trend remainder
Jan 2017 -13.51116 620.0779 -270.566700
Feb 2017 -25.67301 597.0258 266.647194
Mar 2017 -101.08461 573.9738 -19.889167
Apr 2017 219.41788 551.1312 225.450967
May 2017 -83.57931 528.2885 -430.709219
Jun 2017 74.54494 505.7850 219.670064
Jul 2017 30.41916 483.2815 151.299379
Aug 2017 -159.59590 460.6482 111.947695
Sep 2017 -80.86115 438.0150 -243.153796
Oct 2017 -186.38804 418.7237 -67.335678
Nov 2017 96.33461 399.4325 51.232899
Dec 2017 229.97668 398.6426 -196.619290
Jan 2018 -13.51116 397.8527 -41.341565
Feb 2018 -25.67301 405.1831 114.489873
Mar 2018 -101.08461 412.5136 -273.428943
Apr 2018 219.41788 419.9627 -136.380615
May 2018 -83.57931 427.4119 196.167391
Jun 2018 74.54494 433.9023 188.552751
Jul 2018 30.41916 440.3927 116.188144
Aug 2018 -159.59590 449.1809 184.414994
Sep 2018 -80.86115 457.9691 -63.107963
Oct 2018 -186.38804 473.2262 -234.838182
Nov 2018 96.33461 488.4833 -283.817943
Dec 2018 229.97668 494.2690 197.754316
Jan 2019 -13.51116 500.0547 10.456488
Feb 2019 -25.67301 507.5730 -413.900027
Mar 2019 -101.08461 515.0914 233.993203
Apr 2019 219.41788 538.5873 117.994862
May 2019 -83.57931 562.0831 470.496200
Jun 2019 74.54494 586.6381 -6.183045
Jul 2019 30.41916 611.1931 -486.612258
Aug 2019 -159.59590 619.9637 -230.367763
Sep 2019 -80.86115 628.7342 -57.873076
Oct 2019 -186.38804 614.9466 529.441447
Nov 2019 96.33461 601.1590 42.506428
Dec 2019 229.97668 585.6774 -23.654081
Jan 2020 -13.51116 570.1958 284.315323
Feb 2020 -25.67301 561.9913 15.681732
Mar 2020 -101.08461 553.7867 42.297887
Apr 2020 219.41788 546.5084 -222.926267
May 2020 -83.57931 539.2301 -250.650742
Jun 2020 74.54494 530.2467 -414.791602
Jul 2020 30.41916 521.2633 208.317571
Aug 2020 -159.59590 514.3819 -74.786011
Sep 2020 -80.86115 507.5006 357.360600
Oct 2020 -186.38804 504.2319 -232.843895
Nov 2020 96.33461 500.9633 185.702068
Dec 2020 229.97668 500.1919 18.831369
Updated on: 11-Nov-2021
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