I computed AR's models to predict the value of BTC. In order to make the data stationary i have transformed it :

1. yt=log(BTS_t)
2. dyt = yt-yt-1 I take the difference of logarithms (interpretable as the growth rate between two consecutive days). However, I want to extract the forcast of the original value of the BTC.

My code is the following :

Construct data

df_byday <- read_excel("C:/Users/33610/Downloads/dfday.xlsx")
df_byday$log <- log(df_byday$Price)
df_byday$LOGdiff1 <- c(0, diff(df_byday$log, lag = 1))
tsBTC2 <- ts(df_byday$LOGdiff1, start=c(2017,01,01), frequency=365)

Estimate :

summary(mod_ar1)
mod_ma1 <- Arima(tsBTC2, order = c(0, 0, 1))
summary(mod_ma1)
mod_ar8 <- Arima(tsBTC2, order = c(8, 0, 0))
summary(mod_ar8)
mod_arma11 <- Arima(tsBTC2, order = c(1, 0, 1))
summary(mod_arma11)

Correct for non-significant coefficients :

mod_ar8fix <- Arima(tsBTC2, order = c(8, 0, 0), fixed=c(NA,0,0,0,0,0,0,NA,NA))
summary(mod_ar8fix)

Forecast (for the transformed data) :

forecast_ar8 <- forecast(mod_ar8fix, h = 5) 
forecast_ar1 <- forecast(mod_ar1, h = 5)
forecast_ma1 <- forecast(mod_ma1, h = 5)

I want to use this forcast to predict the future value of the BTC and avoid the problem of the log and the difference

Thanks you for your helping!

I computed AR's models to predict the value of BTC. In order to make the data stationary i have transformed it :

1. yt=log(BTS_t)
2. dyt = yt-yt-1 I take the difference of logarithms (interpretable as the growth rate between two consecutive days). However, I want to extract the forcast of the original value of the BTC.

My code is the following :

Construct data

df_byday <- read_excel("C:/Users/33610/Downloads/dfday.xlsx")
df_byday$log <- log(df_byday$Price)
df_byday$LOGdiff1 <- c(0, diff(df_byday$log, lag = 1))
tsBTC2 <- ts(df_byday$LOGdiff1, start=c(2017,01,01), frequency=365)

Estimate :

summary(mod_ar1)
mod_ma1 <- Arima(tsBTC2, order = c(0, 0, 1))
summary(mod_ma1)
mod_ar8 <- Arima(tsBTC2, order = c(8, 0, 0))
summary(mod_ar8)
mod_arma11 <- Arima(tsBTC2, order = c(1, 0, 1))
summary(mod_arma11)

Correct for non-significant coefficients :

mod_ar8fix <- Arima(tsBTC2, order = c(8, 0, 0), fixed=c(NA,0,0,0,0,0,0,NA,NA))
summary(mod_ar8fix)

Forecast (for the transformed data) :

forecast_ar8 <- forecast(mod_ar8fix, h = 5) 
forecast_ar1 <- forecast(mod_ar1, h = 5)
forecast_ma1 <- forecast(mod_ma1, h = 5)

I want to use this forcast to predict the future value of the BTC and avoid the problem of the log and the difference

Thanks you for your helping!

• r
• time-series
• bitcoin
• arima
• forecast

1 Answer 1

Build the log and difference into the fitted model, and the forecast() function will take care of it for you.

e.g.,

library(forecast)

price <- ts(df_byday$Price, start = 2027, frequency = 365)
mod_arima011 <- Arima(price, order = c(0, 1, 1), lambda = 0)
forecast_arima011 <- forecast(mod_arima011, h = 5)

The lambda=0 argument specifies a logarithm. See https://otexts/fpp2/arima.html for a textbook introduction.

But since you're modelling daily data, you would be better off not using a ts object or the forecast package. Instead, consider tsibble objects with the fable package. See https://otexts/fpp3/arima.html.