I have data of total exports of fish from my country in the time period 2010-2020. Since our currency (NOK) has gotten weaker to the currencies we are trading with, we have gotten a lot more value from our exports the last decade.
I have made data who shows how much we have earned from every month the last year. To get precise data for further analysis, I have taken the total value from our exports and divided it by the quantity we have exported.
I want to remove the seasonality of the data. For an example, we will export more every october then january.
This is what I have done:
library(rio)
library(ggplot2)
library(forecast)
library(tseries)
library(tidyverse)
library(zoo)
VM = value/quantity of our exports
data_ma = ts(na.omit(data$VM), frequency = 12)
decomp = stl(data_ma, "periodic")
deseasonal_cnt <- seasadj(decomp)
plot(decomp)
adf.test(data_ma, alternative = "stationary")
Result shows p-value at 0.04
Acf(data_ma, main = '')
Pacf(VM_ma, main = '')
I now want to remove the seasonality, but the I dont know what to do with the ACF and PACF tests as there is so much lags. Can someone please help?
Data:
https://drive.google.com/file/d/17QEiXrV8Zvx5IrIFjBbQGtpODu6ktT8o/view?usp=sharing
R
p-values for adf test are from the t-distribution which is wrong.urdf
fromurca
is probably the most trust worthy function for adf test. It reports the test statistic and critical values. 3) There are various ways to deseasinalize and it is often not straight forward. The most widely used for official statistics is X13Arima or Tramo-Seats. Both are available in R in package... $\endgroup$