| Version: | 1.1.1 |
| Encoding: | UTF-8 |
| Title: | Time Series Analysis Toolkit Based on Symbolic Aggregate Discretization, i.e. SAX |
| Description: | Implements time series z-normalization, SAX, HOT-SAX, VSM, SAX-VSM, RePair, and RRA algorithms facilitating time series motif (i.e., recurrent pattern), discord (i.e., anomaly), and characteristic pattern discovery along with interpretable time series classification. |
| URL: | https://github.com/jMotif/jmotif-R |
| BugReports: | https://github.com/jMotif/jmotif-R/issues |
| Depends: | R (≥ 3.1.0), Rcpp (≥ 0.11.1) |
| Imports: | stats |
| Suggests: | testthat |
| LinkingTo: | Rcpp, RcppArmadillo |
| LazyData: | true |
| SystemRequirements: | C++11 |
| License: | GPL-2 |
| RoxygenNote: | 7.0.2 |
| NeedsCompilation: | yes |
| Packaged: | 2020-02-13 20:43:15 UTC; psenin |
| Author: | Pavel Senin [aut, cre] |
| Maintainer: | Pavel Senin <seninp@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2020-02-13 23:30:09 UTC |
A standard UCR Cylinder-Bell-Funnel dataset from http://www.cs.ucr.edu/~eamonn/time_series_data
Description
A standard UCR Cylinder-Bell-Funnel dataset from http://www.cs.ucr.edu/~eamonn/time_series_data
Usage
CBF
Format
A four-elements list containing train and test data along with their labels
labels_train: the training data labels, correspond to data matrix rows
data_train: the training data matrix, each row is a time series instance
labels_test: the test data labels, correspond to data matrix rows
data_test: the test data matrix, each row is a time series instance
A standard UCR Gun Point dataset from http://www.cs.ucr.edu/~eamonn/time_series_data
Description
A standard UCR Gun Point dataset from http://www.cs.ucr.edu/~eamonn/time_series_data
Usage
Gun_Point
Format
A four-elements list containing train and test data along with their labels
labels_train: the training data labels, correspond to data matrix rows
data_train: the training data matrix, each row is a time series instance
labels_test: the test data labels, correspond to data matrix rows
data_test: the test data matrix, each row is a time series instance
Translates an alphabet size into the array of corresponding SAX cut-lines built using the Normal distribution.
Description
Translates an alphabet size into the array of corresponding SAX cut-lines built using the Normal distribution.
Usage
alphabet_to_cuts(a_size)
Arguments
a_size |
the alphabet size, a value between 2 and 20 (inclusive). |
References
Lonardi, S., Lin, J., Keogh, E., Patel, P., Finding motifs in time series. In Proc. of the 2nd Workshop on Temporal Data Mining (pp. 53-68). (2002)
Examples
alphabet_to_cuts(5)
Computes a TF-IDF weight vectors for a set of word bags.
Description
Computes a TF-IDF weight vectors for a set of word bags.
Usage
bags_to_tfidf(data)
Arguments
data |
the list containing the input word bags. |
References
Senin Pavel and Malinchik Sergey, SAX-VSM: Interpretable Time Series Classification Using SAX and Vector Space Model. Data Mining (ICDM), 2013 IEEE 13th International Conference on, pp.1175,1180, 7-10 Dec. 2013.
Salton, G., Wong, A., Yang., C., A vector space model for automatic indexing. Commun. ACM 18, 11, 613-620, 1975.
Examples
bag1 = data.frame(
"words" = c("this", "is", "a", "sample"),
"counts" = c(1, 1, 2, 1),
stringsAsFactors = FALSE
)
bag2 = data.frame(
"words" = c("this", "is", "another", "example"),
"counts" = c(1, 1, 2, 3),
stringsAsFactors = FALSE
)
ll = list("bag1" = bag1, "bag2" = bag2)
tfidf = bags_to_tfidf(ll)
Computes the cosine similarity between numeric vectors
Description
Computes the cosine similarity between numeric vectors
Usage
cosine_dist(m)
Arguments
m |
the data matrix |
Value
Returns the cosine similarity
Examples
a <- c(2, 1, 0, 2, 0, 1, 1, 1)
b <- c(2, 1, 1, 1, 1, 0, 1, 1)
sim <- cosine_dist(rbind(a,b))
Computes the cosine distance value between a bag of words and a set of TF-IDF weight vectors.
Description
Computes the cosine distance value between a bag of words and a set of TF-IDF weight vectors.
Usage
cosine_sim(data)
Arguments
data |
the list containing a word-bag and the TF-IDF object. |
References
Senin Pavel and Malinchik Sergey, SAX-VSM: Interpretable Time Series Classification Using SAX and Vector Space Model. Data Mining (ICDM), 2013 IEEE 13th International Conference on, pp.1175,1180, 7-10 Dec. 2013.
Salton, G., Wong, A., Yang., C., A vector space model for automatic indexing. Commun. ACM 18, 11, 613-620, 1975.
Finds the Euclidean distance between points, if distance is above the threshold, abandons the computation and returns NAN.
Description
Finds the Euclidean distance between points, if distance is above the threshold, abandons the computation and returns NAN.
Usage
early_abandoned_dist(seq1, seq2, upper_limit)
Arguments
seq1 |
the array 1. |
seq2 |
the array 2. |
upper_limit |
the max value after reaching which the distance computation stops and the NAN is returned. |
A PHYSIONET dataset
Description
A PHYSIONET dataset
Usage
ecg0606
Format
A vector of numeric values
Finds the Euclidean distance between points.
Description
Finds the Euclidean distance between points.
Usage
euclidean_dist(seq1, seq2)
Arguments
seq1 |
the array 1. |
seq2 |
the array 2. stops and the NAN is returned. |
Finds a discord using brute force algorithm.
Description
Finds a discord using brute force algorithm.
Usage
find_discords_brute_force(ts, w_size, discords_num)
Arguments
ts |
the input timeseries. |
w_size |
the sliding window size. |
discords_num |
the number of discords to report. |
References
Keogh, E., Lin, J., Fu, A., HOT SAX: Efficiently finding the most unusual time series subsequence. Proceeding ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Examples
discords = find_discords_brute_force(ecg0606[1:600], 100, 1)
plot(ecg0606[1:600], type = "l", col = "cornflowerblue", main = "ECG 0606")
lines(x=c(discords[1,2]:(discords[1,2]+100)),
y=ecg0606[discords[1,2]:(discords[1,2]+100)], col="red")
Finds a discord (i.e. time series anomaly) with HOT-SAX. Usually works the best with lower sizes of discretization parameters: PAA and Alphabet.
Description
Finds a discord (i.e. time series anomaly) with HOT-SAX. Usually works the best with lower sizes of discretization parameters: PAA and Alphabet.
Usage
find_discords_hotsax(ts, w_size, paa_size, a_size, n_threshold, discords_num)
Arguments
ts |
the input timeseries. |
w_size |
the sliding window size. |
paa_size |
the PAA size. |
a_size |
the alphabet size. |
n_threshold |
the normalization threshold. |
discords_num |
the number of discords to report. |
References
Keogh, E., Lin, J., Fu, A., HOT SAX: Efficiently finding the most unusual time series subsequence. Proceeding ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Examples
discords = find_discords_hotsax(ecg0606, 100, 3, 3, 0.01, 1)
plot(ecg0606, type = "l", col = "cornflowerblue", main = "ECG 0606")
lines(x=c(discords[1,2]:(discords[1,2]+100)),
y=ecg0606[discords[1,2]:(discords[1,2]+100)], col="red")
Finds a discord with RRA (Rare Rule Anomaly) algorithm. Usually works the best with higher than that for HOT-SAX sizes of discretization parameters (i.e., PAA and Alphabet sizes).
Description
Finds a discord with RRA (Rare Rule Anomaly) algorithm. Usually works the best with higher than that for HOT-SAX sizes of discretization parameters (i.e., PAA and Alphabet sizes).
Usage
find_discords_rra(
series,
w_size,
paa_size,
a_size,
nr_strategy,
n_threshold,
discords_num
)
Arguments
series |
the input timeseries. |
w_size |
the sliding window size. |
paa_size |
the PAA size. |
a_size |
the alphabet size. |
nr_strategy |
the numerosity reduction strategy ("none", "exact", "mindist"). |
n_threshold |
the normalization threshold. |
discords_num |
the number of discords to report. |
References
Senin Pavel and Malinchik Sergey, SAX-VSM: Interpretable Time Series Classification Using SAX and Vector Space Model., Data Mining (ICDM), 2013 IEEE 13th International Conference on.
Examples
discords = find_discords_rra(ecg0606, 100, 4, 4, "none", 0.01, 1)
plot(ecg0606, type = "l", col = "cornflowerblue", main = "ECG 0606")
lines(x=c(discords[1,2]:(discords[1,2]+100)),
y=ecg0606[discords[1,2]:(discords[1,2]+100)], col="red")
Get the ASCII letter by an index.
Description
Get the ASCII letter by an index.
Usage
idx_to_letter(idx)
Arguments
idx |
the index. |
Examples
# letter 'b'
idx_to_letter(2)
Compares two strings using mindist.
Description
Compares two strings using mindist.
Usage
is_equal_mindist(a, b)
Arguments
a |
the string a. |
b |
the string b. |
Examples
is_equal_str("aaa", "bbb") # true
is_equal_str("aaa", "ccc") # false
Compares two strings using natural letter ordering.
Description
Compares two strings using natural letter ordering.
Usage
is_equal_str(a, b)
Arguments
a |
the string a. |
b |
the string b. |
Examples
is_equal_str("aaa", "bbb")
is_equal_str("ccc", "ccc")
Get the index for an ASCII letter.
Description
Get the index for an ASCII letter.
Usage
letter_to_idx(letter)
Arguments
letter |
the letter. |
Examples
# letter 'b' translates to 2
letter_to_idx('b')
Get an ASCII indexes sequence for a given character array.
Description
Get an ASCII indexes sequence for a given character array.
Usage
letters_to_idx(str)
Arguments
str |
the character array. |
Examples
letters_to_idx(c('a','b','c','a'))
Converts a set of time-series into a single bag of words.
Description
Converts a set of time-series into a single bag of words.
Usage
manyseries_to_wordbag(data, w_size, paa_size, a_size, nr_strategy, n_threshold)
Arguments
data |
the timeseries data, row-wise. |
w_size |
the sliding window size. |
paa_size |
the PAA size. |
a_size |
the alphabet size. |
nr_strategy |
the NR strategy. |
n_threshold |
the normalization threshold. |
References
Senin Pavel and Malinchik Sergey, SAX-VSM: Interpretable Time Series Classification Using SAX and Vector Space Model. Data Mining (ICDM), 2013 IEEE 13th International Conference on, pp.1175,1180, 7-10 Dec. 2013.
Salton, G., Wong, A., Yang., C., A vector space model for automatic indexing. Commun. ACM 18, 11, 613-620, 1975.
Computes the mindist value for two strings
Description
Computes the mindist value for two strings
Usage
min_dist(str1, str2, alphabet_size, compression_ratio = 1)
Arguments
str1 |
the first string |
str2 |
the second string |
alphabet_size |
the used alphabet size |
compression_ratio |
the distance compression ratio |
Value
Returns the distance between strings
References
Lonardi, S., Lin, J., Keogh, E., Patel, P., Finding motifs in time series. In Proc. of the 2nd Workshop on Temporal Data Mining (pp. 53-68).
Examples
str1 <- c('a', 'b', 'c')
str2 <- c('c', 'b', 'a')
min_dist(str1, str2, 3)
Computes a Piecewise Aggregate Approximation (PAA) for a time series.
Description
Computes a Piecewise Aggregate Approximation (PAA) for a time series.
Usage
paa(ts, paa_num)
Arguments
ts |
a timeseries to compute the PAA for. |
paa_num |
the desired PAA size. |
References
Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S., Dimensionality reduction for fast similarity search in large time series databases. Knowledge and information Systems, 3(3), 263-286. (2001)
Examples
x = c(-1, -2, -1, 0, 2, 1, 1, 0)
x_paa3 = paa(x, 3)
#
plot(x, type = "l", main = c("8-points time series and its PAA transform into three points",
"PAA shown schematically in blue"))
points(x, pch = 16, lwd = 5)
#
paa_bounds = c(1, 1+7/3, 1+7/3*2, 8)
abline(v = paa_bounds, lty = 3, lwd = 2, col = "cornflowerblue")
segments(paa_bounds[1:3], x_paa3, paa_bounds[2:4], x_paa3, col = "cornflowerblue", lwd = 2)
points(x = c(1, 1+7/3, 1+7/3*2) + (7/3)/2, y = x_paa3, pch = 15, lwd = 5, col = "cornflowerblue")
Discretize a time series with SAX using chunking (no sliding window).
Description
Discretize a time series with SAX using chunking (no sliding window).
Usage
sax_by_chunking(ts, paa_size, a_size, n_threshold)
Arguments
ts |
the input time series. |
paa_size |
the PAA size. |
a_size |
the alphabet size. |
n_threshold |
the normalization threshold. |
References
Lonardi, S., Lin, J., Keogh, E., Patel, P., Finding motifs in time series. In Proc. of the 2nd Workshop on Temporal Data Mining (pp. 53-68). (2002)
Generates a SAX MinDist distance matrix (i.e. the "lookup table") for a given alphabet size.
Description
Generates a SAX MinDist distance matrix (i.e. the "lookup table") for a given alphabet size.
Usage
sax_distance_matrix(a_size)
Arguments
a_size |
the desired alphabet size (a value between 2 and 20, inclusive) |
Value
Returns a distance matrix (for SAX minDist) for a specified alphabet size
References
Lonardi, S., Lin, J., Keogh, E., Patel, P., Finding motifs in time series. In Proc. of the 2nd Workshop on Temporal Data Mining (pp. 53-68).
Examples
sax_distance_matrix(5)
Discretizes a time series with SAX via sliding window.
Description
Discretizes a time series with SAX via sliding window.
Usage
sax_via_window(ts, w_size, paa_size, a_size, nr_strategy, n_threshold)
Arguments
ts |
the input timeseries. |
w_size |
the sliding window size. |
paa_size |
the PAA size. |
a_size |
the alphabet size. |
nr_strategy |
the Numerosity Reduction strategy, acceptable values are "exact" and "mindist" – any other value triggers no numerosity reduction. |
n_threshold |
the normalization threshold. |
References
Lonardi, S., Lin, J., Keogh, E., Patel, P., Finding motifs in time series. In Proc. of the 2nd Workshop on Temporal Data Mining (pp. 53-68). (2002)
Transforms a time series into the char array using SAX and the normal alphabet.
Description
Transforms a time series into the char array using SAX and the normal alphabet.
Usage
series_to_chars(ts, a_size)
Arguments
ts |
the timeseries. |
a_size |
the alphabet size. |
References
Lonardi, S., Lin, J., Keogh, E., Patel, P., Finding motifs in time series. In Proc. of the 2nd Workshop on Temporal Data Mining (pp. 53-68). (2002)
Examples
y = c(-1, -2, -1, 0, 2, 1, 1, 0)
y_paa3 = paa(y, 3)
series_to_chars(y_paa3, 3)
Transforms a time series into the string.
Description
Transforms a time series into the string.
Usage
series_to_string(ts, a_size)
Arguments
ts |
the timeseries. |
a_size |
the alphabet size. |
References
Lonardi, S., Lin, J., Keogh, E., Patel, P., Finding motifs in time series. In Proc. of the 2nd Workshop on Temporal Data Mining (pp. 53-68). (2002)
Examples
y = c(-1, -2, -1, 0, 2, 1, 1, 0)
y_paa3 = paa(y, 3)
series_to_string(y_paa3, 3)
Converts a single time series into a bag of words.
Description
Converts a single time series into a bag of words.
Usage
series_to_wordbag(ts, w_size, paa_size, a_size, nr_strategy, n_threshold)
Arguments
ts |
the timeseries. |
w_size |
the sliding window size. |
paa_size |
the PAA size. |
a_size |
the alphabet size. |
nr_strategy |
the NR strategy. |
n_threshold |
the normalization threshold. |
References
Senin Pavel and Malinchik Sergey, SAX-VSM: Interpretable Time Series Classification Using SAX and Vector Space Model. Data Mining (ICDM), 2013 IEEE 13th International Conference on, pp.1175,1180, 7-10 Dec. 2013.
Salton, G., Wong, A., Yang., C., A vector space model for automatic indexing. Commun. ACM 18, 11, 613-620, 1975.
Runs the repair on a string.
Description
Runs the repair on a string.
Usage
str_to_repair_grammar(str)
Arguments
str |
the input string. |
References
N.J. Larsson and A. Moffat. Offline dictionary-based compression. In Data Compression Conference, 1999.
Examples
str_to_repair_grammar("abc abc cba cba bac xxx abc abc cba cba bac")
Extracts a subseries.
Description
Extracts a subseries.
Usage
subseries(ts, start, end)
Arguments
ts |
the input timeseries (0-based, left inclusive). |
start |
the interval start. |
end |
the interval end. |
Examples
y = c(-1, -2, -1, 0, 2, 1, 1, 0)
subseries(y, 0, 3)
Z-normalizes a time series by subtracting its mean and dividing by the standard deviation.
Description
Z-normalizes a time series by subtracting its mean and dividing by the standard deviation.
Usage
znorm(ts, threshold = 0.01)
Arguments
ts |
the input time series. |
threshold |
the z-normalization threshold value, if the input time series' standard deviation will be found less than this value, the procedure will not be applied, so the "under-threshold-noise" would not get amplified. |
References
Dina Goldin and Paris Kanellakis, On similarity queries for time-series data: Constraint specification and implementation. In Principles and Practice of Constraint Programming (CP 1995), pages 137-153. (1995)
Examples
x = seq(0, pi*4, 0.02)
y = sin(x) * 5 + rnorm(length(x))
plot(x, y, type="l", col="blue")
lines(x, znorm(y, 0.01), type="l", col="red")