UNPKG

series.js

Version:

Time Series utilities

github.com/muddydixon/series.js

muddydixon/series.js

140 lines (127 loc) • 6.65 kB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
expect = require('chai').expect Series = require '../src/series.coffee' describe 'series', -> describe 'statistical methods', -> describe 'class methods', -> it 'no accessor', -> dat = [0..9] dat2 = [10..19] expect(Series.lag dat).to.be.deep.eql [null, 0, 1, 2, 3, 4, 5, 6, 7, 8] expect(Series.sum dat).to.be.eql 45 expect(Series.mean dat).to.be.eql 4.5 expect(Series.quantile dat, 0.25).to.be.eql 2.25 expect(Series.median dat).to.be.eql 4.5 expect(Series.sumsq dat).to.be.eql 285 expect(Series.variance dat).to.be.eql 9.166666666666666 expect(Series.variancep dat).to.be.eql 8.25 expect(Series.stdev dat).to.be.eql 3.0276503540974917 expect(Series.stdevp dat).to.be.eql 2.8722813232690143 expect(Series.covariance dat, dat2).to.be.eql 9.166666666666666 expect(Series.covariancep dat, dat2).to.be.eql 8.25 expect(Series.correlation dat, dat2).to.be.eql 0.9999999999999998 expect(Series.autocovariance(dat)).to.be.deep.eql [ 9.166666666666666, 4.166666666666667, 0.2777777777777778 -2.5, -4.166666666666667, -4.722222222222222, -4.166666666666667 -2.5, 0.2777777777777778, 4.166666666666667 ] expect(Series.y()(1)).to.be.eql 1 expect(Series.autocorrelation(dat)).to.be.deep.eql [ 0.9999999999999998, 0.45454545454545453, 0.0303030303030303, -0.2727272727272727, -0.45454545454545453, -0.5151515151515151, -0.45454545454545453, -0.2727272727272727, 0.0303030303030303, 0.45454545454545453 ] expect(Series.y()(1)).to.be.eql 1 it 'with accessor', -> dat = [0..9].map (d)-> {v: d} dat2 = [10..19].map (d)-> {v: d} expect(Series.y((d)-> d.v).lag dat).to.be.deep.eql [null, 0, 1, 2, 3, 4, 5, 6, 7, 8] expect(Series.y()(1)).to.be.eql 1 expect(Series.y((d)-> d.v).sum dat).to.be.eql 45 expect(Series.y()(1)).to.be.eql 1 expect(Series.y((d)-> d.v).mean dat).to.be.eql 4.5 expect(Series.y()(1)).to.be.eql 1 expect(Series.y((d)-> d.v).quantile dat, 0.25).to.be.eql 2.25 expect(Series.y()(1)).to.be.eql 1 expect(Series.y((d)-> d.v).median dat).to.be.eql 4.5 expect(Series.y()(1)).to.be.eql 1 expect(Series.y((d)-> d.v).sumsq dat).to.be.eql 285 expect(Series.y()(1)).to.be.eql 1 expect(Series.y((d)-> d.v).variance dat).to.be.eql 9.166666666666666 expect(Series.y()(1)).to.be.eql 1 expect(Series.y((d)-> d.v).variancep dat).to.be.eql 8.25 expect(Series.y()(1)).to.be.eql 1 expect(Series.y((d)-> d.v).stdev dat).to.be.eql 3.0276503540974917 expect(Series.y()(1)).to.be.eql 1 expect(Series.y((d)-> d.v).stdevp dat).to.be.eql 2.8722813232690143 expect(Series.y()(1)).to.be.eql 1 expect(Series.y((d)-> d.v).covariance dat, dat2).to.be.eql 9.166666666666666 expect(Series.y()(1)).to.be.eql 1 expect(Series.y((d)-> d.v).covariancep dat, dat2).to.be.eql 8.25 expect(Series.y()(1)).to.be.eql 1 expect(Series.y((d)-> d.v).correlation dat, dat2).to.be.eql 0.9999999999999998 expect(Series.y()(1)).to.be.eql 1 expect(Series.y((d)-> d.v).autocovariance(dat)).to.be.deep.eql [ 9.166666666666666, 4.166666666666667, 0.2777777777777778 -2.5, -4.166666666666667, -4.722222222222222, -4.166666666666667 -2.5, 0.2777777777777778, 4.166666666666667 ] expect(Series.y()(1)).to.be.eql 1 expect(Series.y((d)-> d.v).autocorrelation(dat)).to.be.deep.eql [ 0.9999999999999998, 0.45454545454545453, 0.0303030303030303, -0.2727272727272727, -0.45454545454545453, -0.5151515151515151, -0.45454545454545453, -0.2727272727272727, 0.0303030303030303, 0.45454545454545453 ] expect(Series.y()(1)).to.be.eql 1 describe 'class methods', -> before ()-> Series.cleanAccessor() it 'no accessor', -> dat = [0..9] dat2 = [10..19] s = new Series() s.put d for d in dat expect(s.sum()).to.be.eql 45 expect(s.mean()).to.be.eql 4.5 expect(s.quantile 0.25).to.be.eql 2.25 expect(s.median()).to.be.eql 4.5 expect(s.sumsq()).to.be.eql 285 expect(s.variance()).to.be.eql 9.166666666666666 expect(s.variancep()).to.be.eql 8.25 expect(s.stdev()).to.be.eql 3.0276503540974917 expect(s.stdevp()).to.be.eql 2.8722813232690143 expect(s.covariance dat2).to.be.eql 9.166666666666666 expect(s.covariancep dat2).to.be.eql 8.25 expect(s.correlation dat2).to.be.eql 0.9999999999999998 expect(s.autocovariance()).to.be.deep.eql [ 9.166666666666666, 4.166666666666667, 0.2777777777777778 -2.5, -4.166666666666667, -4.722222222222222, -4.166666666666667 -2.5, 0.2777777777777778, 4.166666666666667 ] expect(s.autocorrelation()).to.be.deep.eql [ 0.9999999999999998, 0.45454545454545453, 0.0303030303030303, -0.2727272727272727, -0.45454545454545453, -0.5151515151515151, -0.45454545454545453, -0.2727272727272727, 0.0303030303030303, 0.45454545454545453 ] it 'with accessor', -> dat = [0..9].map (d)-> {v: d} dat2 = [10..19].map (d)-> {v: d} s = new Series().y((d)-> d.v) s.put d for d in dat expect(s.sum()).to.be.eql 45 expect(s.mean()).to.be.eql 4.5 expect(s.quantile 0.25).to.be.eql 2.25 expect(s.median()).to.be.eql 4.5 expect(s.sumsq()).to.be.eql 285 expect(s.variance()).to.be.eql 9.166666666666666 expect(s.variancep()).to.be.eql 8.25 expect(s.stdev()).to.be.eql 3.0276503540974917 expect(s.stdevp()).to.be.eql 2.8722813232690143 expect(s.covariance dat2).to.be.eql 9.166666666666666 expect(s.covariancep dat2).to.be.eql 8.25 expect(s.correlation dat2).to.be.eql 0.9999999999999998 expect(s.autocovariance()).to.be.deep.eql [ 9.166666666666666, 4.166666666666667, 0.2777777777777778 -2.5, -4.166666666666667, -4.722222222222222, -4.166666666666667 -2.5, 0.2777777777777778, 4.166666666666667 ] expect(s.autocorrelation()).to.be.deep.eql [ 0.9999999999999998, 0.45454545454545453, 0.0303030303030303, -0.2727272727272727, -0.45454545454545453, -0.5151515151515151, -0.45454545454545453, -0.2727272727272727, 0.0303030303030303, 0.45454545454545453 ]