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#'@title Anomaly detector using DTW
#'@description Anomaly detection using DTW
#'The DTW is applied to the time series.
#'When seq equals one, observations distant from the closest centroids are labeled as anomalies.
#'When seq is grater than one, sequences distant from the closest centroids are labeled as discords.
#'It wraps the tsclust presented in the dtwclust library.
#'@param seq sequence size
#'@param centers number of centroids
#'@return `hanct_dtw` object
#'@examples
#'library(daltoolbox)
#'
#'#loading the example database
#'data(examples_anomalies)
#'
#'#Using simple example
#'dataset <- examples_anomalies$simple
#'head(dataset)
#'
#'# setting up time series regression model
#'model <- hanct_dtw()
#'
#'# fitting the model
#'model <- fit(model, dataset$serie)
#'
# making detection using hanr_ml
#'detection <- detect(model, dataset$serie)
#'
#'# filtering detected events
#'print(detection[(detection$event),])
#'
#'@export
hanct_dtw <- function(seq = 1, centers=NA) {
obj <- harbinger()
obj$seq <- seq
obj$centers <- centers
class(obj) <- append("hanct_dtw", class(obj))
return(obj)
}
#'@importFrom dtwclust tsclust
#'@importFrom stats na.omit
#'@export
fit.hanct_dtw <- function(obj, serie, ...) {
if (is.na(obj$centers))
obj$centers <- ceiling(log(length(serie), 10))
data <- ts_data(stats::na.omit(serie), obj$seq)
data <- as.data.frame(data)
# Apply k-means
clusters <- dtwclust::tsclust(series = data, type = "partitional", k = obj$centers, distance = "dtw_basic")
centroids <- NULL
for (i in 1:length(clusters@centroids))
centroids <- rbind(centroids, clusters@centroids[[i]])
obj$centroids <- centroids
return(obj)
}
#'@importFrom stats na.omit
#'@export
detect.hanct_dtw <- function(obj, serie, ...) {
if(is.null(serie)) stop("No data was provided for computation", call. = FALSE)
obj <- obj$har_store_refs(obj, serie)
sx <- ts_data(obj$serie, obj$seq)
data <- as.data.frame(sx)
res <- apply(data, 1, function(x) sqrt(min((rowSums(t(obj$centroids - x)^2)))))
res <- obj$har_residuals(res)
anomalies <- obj$har_outliers_idx(res)
anomalies <- obj$har_outliers_group(as.integer(anomalies + obj$seq/2), length(obj$serie))
detection <- obj$har_restore_refs(obj, anomalies = anomalies)
if (obj$seq != 1) {
i <- detection$type=="anomaly"
detection$type[i] <- "discord"
detection$seq[i] <- obj$seq
detection$seqlen[i] <- obj$seq
}
return(detection)
}
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