I have weekly booking date and qty for two years and I need to do analysis of any patter or not so tried using time series

@rumsinha wrote:

for this requirement I approached with time series.

data as below

bookingdate bookingqty

2014-07-27 202

2014-08-03 564

2014-08-10 359

2014-08-17 638

2014-08-24 487

2014-08-31 491

2014-09-07 364

2014-09-14 762

2014-09-21 419

2014-09-28 642

2014-10-05 723

2014-10-12 579

2014-10-19 1803

2014-10-26 437

2014-11-02 587

2014-11-09 803

2014-11-16 1347

2014-11-23 600

2014-11-30 616

2014-12-07 2242

2014-12-14 1313

2014-12-21 264

2014-12-28 918

2015-01-04 420

2015-01-11 741

2015-01-18 2213

2015-01-25 379

2015-02-01 386

2015-02-08 854

2015-02-15 1235

2015-02-22 726

2015-03-01 774

2015-03-08 1135

2015-03-15 1127

2015-03-22 466

2015-03-29 987

2015-04-05 665

2015-04-12 997

2015-04-19 2800

2015-04-26 594

2015-05-03 715

2015-05-10 2009

2015-05-17 1592

2015-05-24 499

2015-05-31 1906

2015-06-07 1619

2015-06-14 1277

2015-06-21 683

2015-06-28 2132

2015-07-05 1195

2015-07-12 1250

2015-07-19 5001

2015-07-26 320

2015-08-02 577

2015-08-09 825

2015-08-16 885

2015-08-23 1910

2015-08-30 1072

2015-09-06 615

2015-09-13 1809

2015-09-20 1243

2015-09-27 1516

2015-10-04 754

2015-10-11 910

2015-10-18 1766

2015-10-25 599

2015-11-01 536

2015-11-08 1170

2015-11-15 2060

2015-11-22 719

2015-11-29 706

2015-12-06 1129

2015-12-13 1807

2015-12-20 949

2015-12-27 653

2016-01-03 1612

2016-01-10 1058

2016-01-17 2699

2016-01-24 617

2016-01-31 335

2016-02-07 527

2016-02-14 526

2016-02-21 1729

2016-02-28 512

2016-03-06 1026

2016-03-13 824

2016-03-20 1144

2016-03-27 711

2016-04-03 743

2016-04-10 847

2016-04-17 833

2016-04-24 4192

2016-05-01 576

2016-05-08 610

2016-05-15 645

2016-05-22 950

2016-05-29 578

2016-06-05 786

2016-06-12 990

2016-06-19 1804

2016-06-26 853

2016-07-03 767

2016-07-10 1325

2016-07-17 1872

2016-07-24 3002

I created time series object as

library(zoo)

pidbookingdata <- zoo(arrangeddata$bookingqty,arrangeddata$bookingdate)

I can see in the x axis index for year 2015 and 2016

but when I use decompose function I get the below error as

decompose(pidbookingdata)
Error in decompose(pidbookingdata) :
time series has no or less than 2 periods

so, from this site I got and tried the below approach which works:

dfts <- as.ts(xts(arrangeddata$bookingqty,order.by=arrangeddata$bookingdate))

dfts

dfts <- ts(dfts, frequency=52)

dcomp <- decompose(dfts)

the dfts data as shown below

dfts

Time Series:
Start = c(1, 1)
End = c(3, 1)
Frequency = 52
[1] 202 564 359 638 487 491 364 762 419 642 723 579 1803 437 587 803 1347 600 616
[20] 2242 1313 264 918 420 741 2213 379 386 854 1235 726 774 1135 1127 466 987 665 997
[39] 2800 594 715 2009 1592 499 1906 1619 1277 683 2132 1195 1250 5001 320 577 825 885 1910
[58] 1072 615 1809 1243 1516 754 910 1766 599 536 1170 2060 719 706 1129 1807 949 653 1612
[77] 1058 2699 617 335 527 526 1729 512 1026 824 1144 711 743 847 833 4192 576 610 645
[96] 950 578 786 990 1804 853 767 1325 1872 3002

when I do plot(dfts)

I see the x axis index as 1.0 1.5 2 2.5 3

am I doing correct or something is wrong here? what does the indices 1.0, 1.5, 2.0 means in terms of weeks?

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