在`ts`、`xts`、`matrix`、`zoo`格式上使用`auto.arima`
englianhu opened this issue · 1 comments
englianhu commented
在ts和xts和matrix格式上使用auto.arima
数据来源：猫城@englianhu/binary.com-interview-question-data/世博量化研究院/文艺数据库/fx/USDJPY/样本2.rds
✔ 1 GiB [世博量化研究院*]❯ 样本2 <- readRDS("~/文档/猫城/binary.com-interview-question-data/文艺数据库/fx/USDJPY/样本2.rds")
✖ 1 GiB [世博量化研究院*]❯ 测试数据 <- 样本2[10000:11200, c('年月日时分', '闭市价')]
✔ 1 GiB [世博量化研究院*]❯ 测试数据 %>% data.frame %>% head
年月日时分 闭市价
1 2015-01-13 22:40:00 117.9290000000000020464
2 2015-01-13 22:41:00 117.8984999999999985221
3 2015-01-13 22:42:00 117.9070000000000106866
4 2015-01-13 22:43:00 117.8985000000000127329
5 2015-01-13 22:44:00 117.8849999999999909051
6 2015-01-13 22:45:00 117.8584999999999922693
✔ 1 GiB [世博量化研究院*]❯
✔ 1 GiB [世博量化研究院*]❯ 测试数据 %>% data.table::as.matrix(rownames = TRUE) %>% auto.arima
Series: .
ARIMA(0,1,0)
sigma^2 = 0.0009413966542781437592: log likelihood = 2485.590000000000145519
AIC=-4969.189999999999599822 AICc=-4969.180000000000291038 BIC=-4964.100000000000363798
✔ 1 GiB [世博量化研究院*]❯ 测试数据 %>% as.xts %>% auto.arima
Series: .
ARIMA(0,1,0)
sigma^2 = 0.0009413966542781437592: log likelihood = 2485.590000000000145519
AIC=-4969.189999999999599822 AICc=-4969.180000000000291038 BIC=-4964.100000000000363798
✔ 1 GiB [世博量化研究院*]❯ 测试数据 %>% as.xts %>% ts %>% auto.arima
Series: .
ARIMA(0,1,0)
sigma^2 = 0.0009413966542781437592: log likelihood = 2485.590000000000145519
AIC=-4969.189999999999599822 AICc=-4969.180000000000291038 BIC=-4964.100000000000363798
✔ 1 GiB [世博量化研究院*]❯ 测试数据 %>% as.xts %>% ts(frequency = 1200) %>% auto.arima
Series: .
ARIMA(0,1,0)
sigma^2 = 0.0009413966542781437592: log likelihood = 2485.590000000000145519
AIC=-4969.189999999999599822 AICc=-4969.180000000000291038 BIC=-4964.100000000000363798
✔ 1 GiB [世博量化研究院*]❯ 测试数据 %>% as.xts %>% ts(frequency = 120) %>% auto.arima
Series: .
ARIMA(0,1,0)
sigma^2 = 0.0009413966542781437592: log likelihood = 2485.590000000000145519
AIC=-4969.189999999999599822 AICc=-4969.180000000000291038 BIC=-4964.100000000000363798
✖ 1 GiB [世博量化研究院*]❯ matrix(测试数据$闭市价, dimnames = list(测试数据$年月日时分, '闭市价'), ncol = 1) %>% auto.arima
Series: .
ARIMA(0,1,0)
sigma^2 = 0.0009413966542781437592: log likelihood = 2485.590000000000145519
AIC=-4969.189999999999599822 AICc=-4969.180000000000291038 BIC=-4964.100000000000363798
✔ 1 GiB [世博量化研究院*]❯ matrix(测试数据$闭市价, dimnames = list(测试数据$年月日时分, '闭市价'), ncol = 1) %>% as.ts %>% auto.arima
Series: .
ARIMA(0,1,0)
sigma^2 = 0.0009413966542781437592: log likelihood = 2485.590000000000145519
AIC=-4969.189999999999599822 AICc=-4969.180000000000291038 BIC=-4964.100000000000363798
✖ 1 GiB [世博量化研究院*]❯ matrix(测试数据$闭市价, dimnames = list(测试数据$年月日时分, '闭市价'), ncol = 1) %>% as.zoo %>% auto.arima
Series: .
ARIMA(0,1,0)
sigma^2 = 0.0009413966542781437592: log likelihood = 2485.590000000000145519
AIC=-4969.189999999999599822 AICc=-4969.180000000000291038 BIC=-4964.100000000000363798测试一下不同函数有何分别，即使设置个frequency，结果都是一样...
✔ 1.5 GiB [世博量化研究院*]❯ microbenchmark(
'as.matrix()' = as.matrix(测试数据, rownames = TRUE) %>% auto.arima,
'as.matrix %>% ' = 测试数据 %>% as.matrix(rownames = TRUE) %>% auto.arima,
'as.xts' = 测试数据 %>% as.xts %>% auto.arima,
'as.xts %>% as.ts' = 测试数据 %>% as.xts %>% ts %>% auto.arima,
'as.ts(freq = 1200)' = 测试数据 %>% as.xts %>% ts(frequency = 1200) %>% auto.arima,
'as.ts(freq = 120)' = 测试数据 %>% as.xts %>% ts(frequency = 120) %>% auto.arima,
'matrix %>% as.ts' = matrix(测试数据$闭市价, dimnames = list(测试数据$年月日时分, '闭市价'), ncol = 1) %>% as.ts %>% auto.arima,
'matrix %>% as.zoo' = matrix(测试数据$闭市价, dimnames = list(测试数据$年月日时分, '闭市价'), ncol = 1) %>% as.zoo %>% auto.arima,
'matrix' = matrix(测试数据$闭市价, dimnames = list(测试数据$年月日时分, '闭市价'), ncol = 1) %>% auto.arima)
Unit: milliseconds
expr min lq mean
as.matrix() 43.51706399999999774764 44.45820700000000158525 51.01456184999999976526
as.matrix %>% 43.60379600000000266391 44.43160650000000089221 52.06498675000000275759
as.xts 42.19390899999999788861 43.44118149999999900501 52.29102436000000153626
as.xts %>% as.ts 42.21445899999999795682 43.00931149999999547617 51.63839049999999986085
as.ts(freq = 1200) 42.24664899999999789770 43.10529650000000145837 51.77428743000000110897
as.ts(freq = 120) 2392.39838700000018434366 2411.68259299999999711872 2462.60519316999989314354
matrix %>% as.ts 41.50966199999999872716 42.22200399999999831380 50.11591734999999658839
matrix %>% as.zoo 46.18885300000000171394 46.91678300000000234604 58.44752450000000010277
matrix 41.58900100000000321643 42.62158850000000143154 49.24968701000000237400
median uq max neval
45.56872500000000059117 48.29254650000000026466 113.00312399999999968259 100
45.77284600000000125419 52.01385399999999492593 98.40117499999999495230 100
44.63580299999999567717 49.53256000000000369710 126.15233999999999525699 100
43.99932599999999638385 49.67516450000000105547 99.18939199999999800639 100
44.28173999999999921329 49.64366749999999939291 121.05843600000000037653 100
2427.76964899999984481838 2455.14120800000000599539 3041.27480900000000474392 100
43.36704900000000151294 47.44317900000000065575 150.78695899999999596730 100
48.47124600000000071987 61.96651900000000523505 222.77181400000000621731 100
43.92246000000000094587 48.42570549999999940383 111.59604899999999361171 100参考资源
englianhu commented
✖ 1.2 GiB [世博量化研究院*]❯ matrix(测试数据$闭市价, dimnames = list(测试数据$年月日时分, '闭市价'), ncol = 1) %>% zoo(frequency = 1) %>% auto.arima
Series: .
ARIMA(0,1,0)
sigma^2 = 0.0009413966542781437592: log likelihood = 2485.590000000000145519
AIC=-4969.189999999999599822 AICc=-4969.180000000000291038 BIC=-4964.100000000000363798
✔ 1.2 GiB [世博量化研究院*]❯ matrix(测试数据$闭市价, dimnames = list(测试数据$年月日时分, '闭市价'), ncol = 1) %>% zoo(frequency = 10) %>% auto.arima
Series: .
ARIMA(0,0,0)(0,1,0)[10]
sigma^2 = 0.0009485104828998929894: log likelihood = 2466.949999999999818101
AIC=-4931.899999999999636202 AICc=-4931.899999999999636202 BIC=-4924.510000000000218279
✔ 1.2 GiB [世博量化研究院*]❯ matrix(测试数据$闭市价, dimnames = list(测试数据$年月日时分, '闭市价'), ncol = 1) %>% zoo(frequency = 12) %>% auto.arima
Series: .
ARIMA(0,0,0)(0,1,0)[12]
sigma^2 = 0.0009501059589014066538: log likelihood = 2462.80999999999994543
AIC=-4923.619999999999890861 AICc=-4923.619999999999890861 BIC=-4916.03999999999996362
✔ 1.2 GiB [世博量化研究院*]❯ matrix(测试数据$闭市价, dimnames = list(测试数据$年月日时分, '闭市价'), ncol = 1) %>% zoo(frequency = 100) %>% auto.arima
Series: .
ARIMA(0,0,0)(0,1,0)[100]
sigma^2 = 0.001026045399758194815: log likelihood = 2280.530000000000200089
AIC=-4559.060000000000400178 AICc=-4559.060000000000400178 BIC=-4549.369999999999890861
✔ 1.2 GiB [世博量化研究院*]❯ matrix(测试数据$闭市价, dimnames = list(测试数据$年月日时分, '闭市价'), ncol = 1) %>% zoo(frequency = 120) %>% auto.arima
Series: .
ARIMA(0,0,0)(0,1,0)[120]
sigma^2 = 0.001045028663398494487: log likelihood = 2239.110000000000127329
AIC=-4476.21000000000003638 AICc=-4476.21000000000003638 BIC=-4466.32999999999992724
✔ 1.2 GiB [世博量化研究院*]❯ matrix(测试数据$闭市价, dimnames = list(测试数据$年月日时分, '闭市价'), ncol = 1) %>% zoo(frequency = 1000) %>% auto.arima
Series: .
ARIMA(0,0,0) with non-zero mean
Coefficients:
mean
117.19461532056617159014422
s.e. 0.01308706771212796507453
sigma^2 = 0.2058672768984180779: log likelihood = -754.5399999999999636202
AIC=1513.07999999999992724 AICc=1513.07999999999992724 BIC=1537.07999999999992724
✔ 1.2 GiB [世博量化研究院*]❯ matrix(测试数据$闭市价, dimnames = list(测试数据$年月日时分, '闭市价'), ncol = 1) %>% zoo(frequency = 1200) %>% auto.arima
Series: .
ARIMA(0,0,0) with non-zero mean
Coefficients:
mean
117.19461532056617159014422
s.e. 0.01308706771212796507453
sigma^2 = 0.2058672768984180779: log likelihood = -754.5399999999999636202
AIC=1513.07999999999992724 AICc=1513.07999999999992724 BIC=1537.44000000000005457以上设置zoo(frequency = 频率)循环周期，运算出的结果不一样...