find_odd_streams: Detect outlying series within a collection of sreaming time...
In oddstream: Outlier Detection in Data Streams

Description Usage Arguments Value References See Also Examples

View source: R/find_odd_streams.R

This function detect outlying series within a collection of streaming time series. A sliding window is used to handle straming data. In the precence of concept drift, the forecast boundary for the system's typical behaviour can be updated periodically.

find_odd_streams(train_data, test_stream, update_threshold = TRUE,
  window_length = nrow(train_data), window_skip = window_length,
  concept_drift = FALSE, trials = 500, p_rate = 0.001,
  cd_alpha = 0.05)

`train_data`	A multivariate time series data set that represents the typical behaviour of the system.
`test_stream`	A multivariate streaming time series data set to be tested for outliers
`update_threshold`	If TRUE, the threshold value to determine outlying series is updated. The default value is set to TRUE
`window_length`	Sliding window size (Ideally this window length should be equal to the length of the training multivariate time series data set that is used to define the outlying threshold)
`window_skip`	The number of steps the window should slide forward. The default is set to window_length
`concept_drift`	If TRUE, The outlying threshold will be updated after each window. The default is set to FALSE
`trials`	Input for `set_outlier_threshold` function. Default value is set to 500.
`p_rate`	False positive rate. Default value is set to 0.001.
`cd_alpha`	Singnificance level for the test of non-stationarity.

a list with components

`out_marix`	The indices of the outlying series in each window
`p_value`	p-value for the two sample comparison test for concept drift detection
`anom_threshold`	anomalous threshold

For each window a plot is also produced on the current graphic device

Clifton, D. A., Hugueny, S., & Tarassenko, L. (2011). Novelty detection with multivariate extreme value statistics. Journal of signal processing systems, 65 (3),371-389.

Duong, T., Goud, B. & Schauer, K. (2012) Closed-form density-based framework for automatic detection of cellular morphology changes. PNAS, 109, 8382-8387.

Talagala, P., Hyndman, R., Smith-Miles, K., Kandanaarachchi, S., & Munoz, M. (2018). Anomaly detection in streaming nonstationary temporal data (No. 4/18). Monash University, Department of Econometrics and Business Statistics.

extract_tsfeatures, get_pc_space, set_outlier_threshold, gg_featurespace

#Generate training dataset
set.seed(890)
nobs = 250
nts = 100
train_data <- ts(apply(matrix(ncol = nts, nrow = nobs), 2, function(nobs){10 + rnorm(nobs, 0, 3)}))
# Generate test stream with some outliying series
nobs = 15000
test_stream <- ts(apply(matrix(ncol = nts, nrow = nobs), 2, function(nobs){10 + rnorm(nobs, 0, 3)}))
test_stream[360:1060, 20:25] = test_stream[360:1060, 20:25] * 1.75
test_stream[2550:3550, 20:25] =  test_stream[2550:3550, 20:25] * 2
find_odd_streams(train_data, test_stream , trials = 100)


# Considers the first window  of the data set as the training set and the remaining as
# the test stream

train_1data <- anomalous_stream[1:100,]
test_stream <-anomalous_stream[101:1456,]
find_odd_streams(train_data, test_stream , trials = 100)