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✖ 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...
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
测试一下不同函数有何分别,即使设置个
frequency
,结果都是一样...参考资源