Simpan hasil perkiraan dalam file csv di R

df=structure(list(X.1 = 1:6, X = c(1L, 1L, 1L, 1L, 1L, 1L), json_data.time.updated = structure(1:6, .Label = c("Jan 19, 2019 15:18:00 UTC", 
"Jan 19, 2019 15:19:00 UTC", "Jan 19, 2019 15:51:00 UTC", "Jan 19, 2019 15:52:00 UTC", 
"Jan 19, 2019 15:54:00 UTC", "Jan 19, 2019 15:55:00 UTC"), class = "factor"), 
    json_data.time.updatedISO = structure(1:6, .Label = c("2019-01-19T15:18:00+00:00", 
    "2019-01-19T15:19:00+00:00", "2019-01-19T15:51:00+00:00", 
    "2019-01-19T15:52:00+00:00", "2019-01-19T15:54:00+00:00", 
    "2019-01-19T15:55:00+00:00"), class = "factor"), json_data.time.updateduk = structure(1:6, .Label = c("Jan 19, 2019 at 15:18 GMT", 
    "Jan 19, 2019 at 15:19 GMT", "Jan 19, 2019 at 15:51 GMT", 
    "Jan 19, 2019 at 15:52 GMT", "Jan 19, 2019 at 15:54 GMT", 
    "Jan 19, 2019 at 15:55 GMT"), class = "factor"), code = structure(c(1L, 
    1L, 1L, 1L, 1L, 1L), .Label = "USD", class = "factor"), rate = structure(c(2L, 
    3L, 6L, 1L, 5L, 4L), .Label = c("3,735.3200", "3,735.7750", 
    "3,735.9150", "3,736.0750", "3,736.7717", "3,736.9100"), class = "factor"), 
    description = structure(c(1L, 1L, 1L, 1L, 1L, 1L), .Label = "United States Dollar", class = "factor"), 
    rate_float = structure(c(2L, 3L, 6L, 1L, 5L, 4L), .Label = c("3735.32", 
    "3735.775", "3735.915", "3736.075", "3736.7717", "3736.91"
    ), class = "factor")), class = "data.frame", row.names = c(NA, 
-6L))

-

require(rugarch)


#We can then compute the ARMA(1,1)-GARCH(1,1) model as an example:



  spec <- ugarchspec(variance.model = list(model = "sGARCH", 
                                           garchOrder = c(1, 1), 
                                           submodel = NULL, 
                                           external.regressors = NULL, 
                                           variance.targeting = FALSE), 

                     mean.model     = list(armaOrder = c(1, 1), 
                                           external.regressors = NULL, 
                                           distribution.model = "norm", 
                                           start.pars = list(), 
                                           fixed.pars = list()))

garch <- ugarchfit(spec = spec, data = df$rate_float, solver.control = list(trace=0))


ugarchforecast(garch, n.ahead = 5)

Saya akan menjalankan skrip perkiraan setiap 5 menit dari baris spec <- ugarchspec (variance.model = list (model = "sGARCH ini, misalnya skrip diluncurkan pada 10:10, perkiraan dibuat dalam 5 langkah, hasil ini harus ditulis ke dalam file csv

kemudian diluncurkan pada 10:15, ramalan dibuat dengan 5 langkah, maka hasil ini harus ditulis ke dalam file csv yang diberi tanda tanggal

lalu pada pukul 10.20 dan seterusnya. Bagaimana cara menambahkan prediksi ke satu csv dengan tanda tanggal setiap kali skrip dijalankan?

outputnya bisa jadi masukkan deskripsi gambar di sini


r dataframe time-series
person cbool    schedule 28.01.2019    source sumber
comment
Sys.time() akan memberi Anda tanggal+waktu ketika perintah diluncurkan, gunakan cbind() untuk menyimpan tanggal waktu dan prediksi mungkin?   -  person RLave    schedule 28.01.2019
comment
final <- cbind(datetime=Sys.time(), steps=my_predicitons) misalnya   -  person RLave    schedule 28.01.2019
comment
@RLave, my_predicitons itu nama file csv?   -  person cbool    schedule 28.01.2019
comment
bukan, itu vektor prediksi, kolom step Anda   -  person RLave    schedule 28.01.2019

Jawaban (1)


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Sebagai contoh saja:

linmod <- lm(mpg ~ hp, data = mtcars) # your model

predictions <- predict(linmod) # your vector of predictions

Sebelum menyimpan cukup cbind() dengan waktu:

final <- cbind(date_time=format(Sys.time(), format="%Y/%m/%d %H:%M"),
               predictions = predictions)

Sekarang Anda dapat menyimpan final dengan write.csv().

person RLave    schedule 28.01.2019