Plot top 3 anomalous time series by bivariate kernel density

plot(dat0[, hdr])
Error in plot(dat0[, hdr]) :
error in evaluating the argument 'x' in selecting a method for function 'plot': Error in [.default(dat0, , hdr) : invalid subscript type 'list'

dat0 looks ok

summary(hdr)
Length Class Mode
index 3 -none- numeric
scores 88 -none- numeric

head(hdr)
$index
[1] 25 22 42

$scores
[,1] [,2]
[1,] -3.41964341 -1.89456155
[2,] 0.87844942 2.12278092
[3,] -2.23081484 -1.13704932
[4,] 0.35507648 -1.51430496
...

The first plots looked ok.

sessionInfo()
R version 3.2.0 (2015-04-16)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 14.04.2 LTS

locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8
[4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] stats graphics grDevices utils datasets methods base

other attached packages:
[1] anomalousACM_0.1.0 alphahull_2.0 splancs_2.01-37 sp_1.1-0 spatstat_1.42-0
[6] sgeostat_1.0-25 tripack_1.3-6 ggplot2_1.0.1 ForeCA_0.2.2 ifultools_2.0-1
[11] MASS_7.3-39 splus2R_1.2-0 devtools_1.8.0

loaded via a namespace (and not attached):
[1] pcaPP_1.9-60 Rcpp_0.11.6 sapa_2.0-1 git2r_0.10.1 plyr_1.8.2
[6] ks_1.9.4 bitops_1.0-6 tools_3.2.0 digest_0.6.8 goftest_1.0-2
[11] memoise_0.2.1 gtable_0.1.2 nlme_3.1-120 lattice_0.20-31 mgcv_1.8-6
[16] Matrix_1.2-0 RcppRoll_0.2.2 mvtnorm_1.0-2 proto_0.3-10 httr_0.6.1
[21] stringr_1.0.0 rversions_1.0.0 grid_3.2.0 hdrcde_3.1 rgl_0.95.1247
[26] XML_3.98-1.2 polyclip_1.3-2 reshape2_1.4.1 deldir_0.1-9 magrittr_1.5
[31] tensor_1.5 scales_0.2.4 misc3d_0.8-4 abind_1.4-3 colorspace_1.2-6
[36] KernSmooth_2.23-14 stringi_0.4-1 RCurl_1.95-4.6 munsell_0.4.2

Hi,

When using the anomalous package on a single time series, the following error was encountered (It requires at least two timeseries to work properly)

Error in apply(nay, 2, all) : dim(X) must have a positive length

Thanks !

Could you please share the groud true labels of your Yahoo real datasets, synthetic datasets or the datasets in the folder 'data' on this website?

Hi,
Is there a way to detect outliers taking in account more than the first 2 PC? For example PC1 + PC2 + PC3 + PC4 if I realise that I need to use all of them to cover a good enough cumulated variance of my original dataset. Would it be possible to modify your source code to make it work?
Tx

curl: (56) OpenSSL SSL_read: SSL_ERROR_SYSCALL, errno 104
Error in download.file(url, destfile, method, mode = "wb", ...) :
'curl' call had nonzero exit status
Warning in download.packages(x$name, destdir = dest_dir, repos = x$repos, :
download of package ‘rgl’ failed
Installation failed: subscript out of bounds
'/home/test/anaconda3/lib/R/bin/R' --no-site-file --no-environ --no-save
--no-restore --quiet CMD INSTALL '/tmp/RtmpNgn3Bc/devtools2f8643039054/ks'
--library='/home/test/anaconda3/lib/R/library' --install-tests

ERROR: dependency ‘rgl’ is not available for package ‘ks’

• removing ‘/home/test/anaconda3/lib/R/library/ks’
  Installation failed: Command failed (1)
  '/home/test/anaconda3/lib/R/bin/R' --no-site-file --no-environ --no-save
  --no-restore --quiet CMD INSTALL
  '/tmp/RtmpNgn3Bc/devtools2f86109209dc/hdrcde'
  --library='/home/test/anaconda3/lib/R/library' --install-tests

ERROR: dependency ‘ks’ is not available for package ‘hdrcde’

• removing ‘/home/test/anaconda3/lib/R/library/hdrcde’
  Installation failed: Command failed (1)
  '/home/test/anaconda3/lib/R/bin/R' --no-site-file --no-environ --no-save
  --no-restore --quiet CMD INSTALL
  '/tmp/RtmpNgn3Bc/devtools2f867c367d45/robjhyndman-anomalous-acm-79d50aa'
  --library='/home/test/anaconda3/lib/R/library' --install-tests

ERROR: dependencies ‘alphahull’, ‘hdrcde’ are not available for package ‘anomalousACM’

• removing ‘/home/test/anaconda3/lib/R/library/anomalousACM’
  Installation failed: Command failed (1)

similar error while installing cut down version
My OS is ubuntu 17.04

In line 184 of tsmeasures.R, you have:

      trend0 <- fitted(mgcv::gam(contx ~ s(tt)))
      remainder <- contx - trend0
      deseason <- contx - trend0
v.adj <- var(trend0, na.rm = TRUE)

It seems to me that it needs to be:
v.adj <- var(remainder, na.rm = TRUE)

instead. This would be in line with the seasonal case and with a slide deck that I read on the topic (http://robjhyndman.com/seminars/big-time-series/). Also, I was getting trends of 1 for series that don't seem to have a trend, and trend of 0 with series that seem to have one.

Could you add installation instructions in the readme? Is it devtools::install_github(), etc

Thanks for your work! This package is awesome

Hi,

When using the anomalous package for shorter time series (i.e below than 2*frequency), the package gave me an error stating that it cannot compute the KL score "I cannot compute KLscore when the length is too small.". When I debugged the code, I found the relevant constraint in [1], which doesn't accommodate for shorter time series that violates the aforementioned condition. Although ideally, it should log a warning message and continue with the rest of the computation. But according to [2], it causes to stop the program abruptly, without logging the computed values of other features (i.e other than KL Score)

Thanks !

[1] https://github.com/robjhyndman/anomalous/blob/master/R/tsmeasures.R#L204
[2] https://github.com/robjhyndman/anomalous/blob/master/R/tsmeasures.R#L205

Hi, any ideas why the installation isn't working?
Below is the output I get. the "plain" anomalous package works BTW.
thanks

> devtools::install_github('robjhyndman/anomalous-acm')
Downloading GitHub repo robjhyndman/anomalous-acm@master
Installing anomalousACM
Skipping 1 packages not available: spatstat
'/Library/Frameworks/R.framework/Resources/bin/R' --no-site-file --no-environ --no-save --no-restore CMD INSTALL  \
  '/private/var/folders/t3/f4r4rwmn7xl80skj6h35ftnh0000gn/T/RtmpiKihGU/devtools2cb837be8a52/robjhyndman-anomalous-acm-10373a7'  \
  --library='/Library/Frameworks/R.framework/Versions/3.2/Resources/library' --install-tests 

* installing *source* package ‘anomalousACM’ ...
** R
** data
*** moving datasets to lazyload DB
** demo
** inst
** tests
** byte-compile and prepare package for lazy loading
Warning: package ‘ggplot2’ was built under R version 3.2.3
Error : package ‘spatstat’ required by ‘alphahull’ could not be found
ERROR: lazy loading failed for package ‘anomalousACM’
* removing ‘/Library/Frameworks/R.framework/Versions/3.2/Resources/library/anomalousACM’
Error: Command failed (1)

Hi there

I am trying to run the example stated in README file and got the results:

> z <- ts(matrix(rnorm(3000),ncol=100),freq=4)
> y <- tsmeasures(z)
> biplot.features(y)
Error in pcaPP::PCAproj(naomit.x, k = 2, scale = sd, center = mean) : 
  object 'C_pcaProj_up' not found
> anomaly(y)
Error in pcaPP::PCAproj(naomit.x, k = 2, center = mean, scale = sd) : 
  object 'C_pcaProj_up' not found

Google indeed gave me quite strange results on that (all pointing to fixing Win 10 bugs etc.)

Hi,

I noticed that the tsmeasures function will terminate with an error if run on constant time series as follows:

> tsmeasures(cbind(rep(3,50), rep(4,50)))
Error in acf(x, plot = FALSE, na.action = na.exclude) : 'lag.max' must be at least 0 In addition: Warning message: In acf(x, plot = FALSE, na.action = na.exclude) : NAs introduced by coercion

The problem is that in the following line in the tsmeasures function:

if (normalise) {
x <- as.ts(scale(x, center = TRUE, scale = TRUE)) # Normalise data to mean = 0, sd = 1
}

if the series is constant, scale will divide by 0, so that you get a series consisting of NaN. Then, later on, when acf() is executed on this NaN series, it throws the error.

Solution would be probably to check in the normalization if the series is constant, and if it is, not use the scale.

i.e., instead of:
x <- as.ts(scale(x, center = TRUE, scale = TRUE))

you could use something like:

x <- as.ts(scale(x, center = TRUE, scale = !(abs(max(x) - min(x)) < 1e9)))

I want to evaluate the result of ahull in terms of AUC. But it needs to rank all samples in order of anomalous level . How can I achieve it? I try setting the value of parameter n to be the number of samples and ordered=TRUE in function 'anomaly' but an error occured: 'Error in findnextoutlier(scores, nextout$alpha, tmp.idx[1:(i - 1)]) : Too hard. Please reduce the number of outliers requeste'. (There are no errors when I set 'method=hdr' but I want to use ahull). Does anybody help? Thank you very much!