Install Packagaes

library(forecast)

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library(TTR)
library(readxl)
library(tseries)
library(TTR)
library(TSA)

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##   fitted.Arima forecast
##   plot.Arima   forecast

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## Attaching package: 'TSA'

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library(ggplot2)
library(kableExtra)
library(imputeTS)

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library(tseries)
library(dplyr)

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library(tidyverse)

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library(graphics)
library(lmtest)

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Latar Belakang

Data yang digunakan pada proses forecasting adalah Kurs Transaksi Mata Uang yang dilakukan oleh Bank Indonesia pada rentang periode 1 tahun dengan perhitungan data harian, 1 Januari hingga 31 Desember 2021 terhadap mata uang Britania Raya, Pound sterling

Import Data

data <- read_excel("~/Downloads/kurs transaksi GBP.xlsx")
ts.data <- ts(data)
head(data)

## # A tibble: 6 × 2
##   Tanggal             Kurs_Jual
##   <dttm>                  <dbl>
## 1 2021-01-04 00:00:00    19128.
## 2 2021-01-05 00:00:00    19038.
## 3 2021-01-06 00:00:00    19044.
## 4 2021-01-07 00:00:00    19024.
## 5 2021-01-08 00:00:00    19158.
## 6 2021-01-11 00:00:00    19225.

str(data)

## tibble [256 × 2] (S3: tbl_df/tbl/data.frame)
##  $ Tanggal  : POSIXct[1:256], format: "2021-01-04" "2021-01-05" ...
##  $ Kurs_Jual: num [1:256] 19128 19038 19044 19024 19158 ...

Eksplorasi Data

data$Tanggal <- as.Date(data$Tanggal)
ggplot(data, aes(x=Tanggal, y= Kurs_Jual))+
  geom_line()+
  scale_x_date(date_breaks = "2 month", date_labels = "%Y %b %d") +
  labs(title = "Plot Time Series Data Kurs Transaksi GBP",
       subtitle = "(Januari 2021 sd Desember 2021)",
       y="Kurs Jual GBP") +
  theme(plot.title =  element_text(face = "bold", hjust=.5),
        plot.subtitle = element_text(hjust=.5))

## Time Series Plot Data Training dan Testing

p=0.8
freq_train=as.integer(p*nrow(data))
data$Tanggal <- as.Date(data$Tanggal)
ggplot(data, aes(x=Tanggal, y=Kurs_Jual)) +
  geom_line() + 
  scale_x_date(date_breaks = "2 month", date_labels = "%Y %b %d") +
  labs(title = "Plot Time Series Data Kurs Transaksi GBP",
       subtitle = "(Januari 2021 sd Desember 2021)",
       y="Kurs Jual GBP") +
  geom_vline(aes(xintercept = Tanggal[freq_train], 
                 col="Frequency_Train_Data"), lty=2, lwd=.7) +
  theme(plot.title =  element_text(face = "bold", hjust=.5),
        plot.subtitle = element_text(hjust=.5),
        legend.position = "bottom") +
  scale_color_manual(name = "", values = c(Frequency_Train_Data="red"))

data[freq_train,1]

## # A tibble: 1 × 1
##   Tanggal   
##   <date>    
## 1 2021-10-19