在`ts`、`xts`、`matrix`、`zoo`格式上使用`auto.arima` about binary.com-interview-question HOT 1 OPEN

✖ 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