Package matrixprofile computes the matrix profile and matrix profile index of a time series
sig := []float64{0, 0.99, 1, 0, 0, 0.98, 1, 0, 0, 0.96, 1, 0}
mp, err := New(sig, nil, 4)
if err != nil {
panic(err)
}
if err = mp.Compute(nil); err != nil {
panic(err)
}
fmt.Printf("Signal: %.3f\n", sig)
fmt.Printf("Matrix Profile: %.3f\n", mp.MP)
fmt.Printf("Profile Index: %5d\n", mp.Idx)
Output: Signal: [0.000 0.990 1.000 0.000 0.000 0.980 1.000 0.000 0.000 0.960 1.000 0.000] Matrix Profile: [0.014 0.014 0.029 0.029 0.014 0.014 0.029 0.029 0.029] Profile Index: [ 4 5 6 7 0 1 2 3 4]
sin := siggen.Sin(1, 5, 0, 0, 100, 2)
sin2 := siggen.Sin(0.25, 10, 0, 0.75, 100, 0.25)
saw := siggen.Sawtooth(0.5, 7, 0, 0, 100, 1)
line := siggen.Line(0, 0, len(sin2)*4)
sig := siggen.Append(sin, sin2, sin, line, sin2, line, sin2, line, saw)
noise := siggen.Noise(0.1, len(sig))
sig = siggen.Add(sig, noise)
var m int
m = 32
mp, err := New(sig, nil, m)
if err != nil {
panic(err)
}
ao := NewAnalyzeOpts()
ao.OutputFilename = "mp_sine.png"
if err = mp.Analyze(nil, ao); err != nil {
panic(err)
}
fmt.Printf("Saved png file result to %s\n", ao.OutputFilename)
Output: Saved png file result to mp_sine.png
sin := siggen.Sin(1, 4, 0, 0, 100, 0.25)
saw := siggen.Sawtooth(1, 4, 0, 0, 100, 0.25)
square := siggen.Square(1, 4, 0, 0, 100, 0.25)
line := siggen.Line(0, 0, len(sin)*4)
line2 := siggen.Line(0, 0, len(sin)*3)
sig := make([][]float64, 3)
sig[0] = siggen.Append(line, line, line, saw, line2, saw, line2)
sig[1] = siggen.Append(line, sin, line2, sin, line2, sin, line2, sin, line2)
sig[2] = siggen.Append(line, square, line2, square, line2, square, line2, square, line2)
sig[0] = siggen.Add(sig[0], siggen.Noise(0.1, len(sig[0])))
sig[1] = siggen.Add(sig[1], siggen.Noise(0.1, len(sig[0])))
sig[2] = siggen.Add(sig[2], siggen.Noise(0.1, len(sig[0])))
m := 25
mp, err := NewKMP(sig, m)
if err != nil {
panic(err)
}
if err = mp.Compute(); err != nil {
panic(err)
}
if err = mp.Visualize("mp_kdim.png"); err != nil {
panic(err)
}
fmt.Println("Saved png file result to mp_kdim.png")
Output: Saved png file result to mp_kdim.png
This section is empty.
This section is empty.
MPDist computes the matrix profile distance measure between a and b with a subsequence window of m.
const ( AlgoSTOMP Algo = "stomp" AlgoSTAMP Algo = "stamp" AlgoSTMP Algo = "stmp" AlgoMPX Algo = "mpx" )
type AnalyzeOpts struct {
OutputFilename string
}
AnalyzeOpts contains all the parameters needed for basic features to discover from a matrix profile. This is currently limited to motif, discord, and segmentation discovery.
NewAnalyzeOpts creates a default set of parameters to analyze the matrix profile.
KMP is a struct that tracks the current k-dimensional matrix profile computation for a given slice of timeseries of length N and subsequence length of M. The profile and the profile index are stored here.
NewKMP creates a matrix profile struct specifically to be used with the k dimensional matrix profile computation. The number of rows represents the number of dimensions, and each row holds a series of points of equal length as each other.
Analyze has not been implemented yet
Compute runs a k dimensional matrix profile calculation across all time series
DiscoverDiscords has not been implemented yet
DiscoverMotifs has not been implemented yet
DiscoverSegments has not been implemented yet
Load will attempt to load a matrix profile from a file for iterative use
Save will save the current matrix profile struct to disk
Visualize creates a png of the k-dimensional matrix profile.
type MPOpts struct {
Algorithm Algo `json:"algorithm"`
SamplePct float64 `json:"sample_pct"`
NJobs int `json:"n_jobs"`
Euclidean bool `json:"euclidean"`
RemapNegCorr bool `json:"remap_negative_correlation"`
}
MPOpts are parameters to vary the algorithm to compute the matrix profile.
NewMPOpts returns a default MPOpts
MatrixProfile is a struct that tracks the current matrix profile computation for a given timeseries of length N and subsequence length of W. The profile and the profile index are stored here.
New creates a matrix profile struct with a given timeseries length n and subsequence length of m. The first slice, a, is used as the initial timeseries to join with the second, b. If b is nil, then the matrix profile assumes a self join on the first timeseries.
Analyze performs the matrix profile computation and discovers various features from the profile such as motifs, discords, and segmentation. The results are visualized and saved into an output file.
ApplyAV applies an annotation vector to the current matrix profile. Annotation vector values must be between 0 and 1.
Compute calculate the matrixprofile given a set of input options.
DiscoverDiscords finds the top k time series discords starting indexes from a computed matrix profile. Each discovery of a discord will apply an exclusion zone around the found index so that new discords can be discovered.
DiscoverMotifs will iteratively go through the matrix profile to find the top k motifs with a given radius. Only applies to self joins.
// generate a signal mainly composed of sine waves and switches
// frequencies, amplitude, and offset midway through
// amplitude of 1, frequency of 5Hz, sampling frequency of 100 Hz,
// time of 2 seconds
sin := siggen.Sin(1, 5, 0, 0, 100, 2)
// amplitude of 0.25, frequency of 10Hz, offset of 0.75, sampling
// frequency of 100 Hz, time of 1 second
sin2 := siggen.Sin(0.25, 10, 0, 0.75, 100, 1)
sig := siggen.Append(sin, sin2)
// create a new MatrixProfile struct using the signal and a
// subsequence length of 32. The second subsequence is set to nil
// so we perform a self join.
mp, err := New(sig, nil, 32)
if err != nil {
panic(err)
}
// run the STMP algorithm with self join. The matrix profile
// will be stored in mp.MP and the matrix profile index will
// be stored in mp.Idx
o := NewMPOpts()
o.Algorithm = AlgoSTMP
if err = mp.Compute(o); err != nil {
panic(err)
}
// finds the top 3 motifs in the signal. Motif groups include
// all subsequences that are within 2 times the distance of the
// original motif pair
motifs, err := mp.DiscoverMotifs(2, 2, 10, mp.W/2)
if err != nil {
panic(err)
}
for i, mg := range motifs {
fmt.Printf("Motif Group %d\n", i)
fmt.Printf(" %d motifs\n", len(mg.Idx))
}
Output: Motif Group 0 2 motifs Motif Group 1 2 motifs
DiscoverSegments finds the the index where there may be a potential timeseries change. Returns the index of the potential change, value of the corrected arc curve score and the histogram of all the crossings for each index in the matrix profile index. This approach is based on the UCR paper on segmentation of timeseries using matrix profiles which can be found https://www.cs.ucr.edu/%7Eeamonn/Segmentation_ICDM.pdf
// generate a signal mainly composed of sine waves and switches
// frequencies, amplitude, and offset midway through
// amplitude of 1, frequency of 5Hz, sampling frequency of 100 Hz,
// time of 2 seconds
sin := siggen.Sin(1, 5, 0, 0, 100, 2)
// amplitude of 0.25, frequency of 10Hz, offset of 0.75, sampling
// frequency of 100 Hz, time of 1 second
sin2 := siggen.Sin(0.25, 10, 0, 0.75, 100, 1)
sig := siggen.Append(sin, sin2)
// noise with an amplitude of 0.1
noise := siggen.Noise(0.01, len(sig))
sig = siggen.Add(sig, noise)
// create a new MatrixProfile struct using the signal and a
// subsequence length of 32. The second subsequence is set to nil
// so we perform a self join.
mp, err := New(sig, nil, 32)
if err != nil {
panic(err)
}
// run the STMP algorithm with self join. The matrix profile
// will be stored in mp.MP and the matrix profile index will
// be stored in mp.Idx
o := NewMPOpts()
o.Algorithm = AlgoSTMP
if err = mp.Compute(o); err != nil {
panic(err)
}
// segment the timeseries using the number of arc crossings over
// each index in the matrix profile index
idx, cac, _ := mp.DiscoverSegments()
fmt.Printf("Signal change foud at index: %d\n", idx)
fmt.Printf("Corrected Arc Curve (CAC) value: %.3f\n", cac)
Output: Signal change foud at index: 194 Corrected Arc Curve (CAC) value: 0.000
Load will attempt to load a matrix profile from a file for iterative use
Save will save the current matrix profile struct to disk
Update updates a matrix profile and matrix profile index in place providing streaming like behavior.
Visualize creates a png of the matrix profile given a matrix profile.
type MotifGroup struct {
Idx []int
MinDist float64
}
MotifGroup stores a list of indices representing a similar motif along with the minimum distance that this set of motif composes of.
type PMP struct {
A []float64 `json:"a"`
B []float64 `json:"b"`
SelfJoin bool `json:"self_join"`
PMP [][]float64 `json:"pmp"`
PIdx [][]int `json:"ppi"`
PWindows []int `json:"windows"`
Opts *PMPOpts `json:"options"`
}
PMP represents the pan matrix profile
NewPMP creates a new Pan matrix profile
Analyze has not been implemented yet
Compute calculate the pan matrixprofile given a set of input options.
DiscoverDiscords has not been implemented yet
DiscoverMotifs has not been implemented yet
DiscoverSegments has not been implemented yet
Load will attempt to load a matrix profile from a file for iterative use
Save will save the current matrix profile struct to disk
Visualize has not been implemented yet
type PMPOpts struct {
LowerM int `json:"lower_m"`
UpperM int `json:"upper_m"`
MPOpts *MPOpts `json:"mp_options"`
}
PMPOpts are parameters to vary the algorithm to compute the pan matrix profile.
NewPMPOpts returns a default PMPOpts
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4