4-Automatic_ARIMA_model.Rmd

---
title: "Automatic_ARIMA"
author: "Elias Mayer"
date: "20 8 2021"
output:
  word_document: default
  html_document: default
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

1. Simple batch ARIMA model evaluated against different other forecasting models (CV - snaive) for more details see report: https://github.com/EliDerDeli/Forecasting-M3-experiments/blob/main/Forecast_report.pdf 

```{r arima}
library(Mcomp)
library(forecast)
library(dplyr)
library(tsibble)
library(ggplot2)
library(ggthemes)
library(stringr)


#------------------------------------------------------------


arr_ids <- array(NA,c(70,1))

count = 1 


for (ex in 1:length(M3)){ 
    

   if ((M3[[ex]]$period == "QUARTERLY") & (ex >= 701) & (ex <= 1400) & (ex %% 10 == 2)) {
     
    arr_ids[count,1] <- ex
       
    count = count + 1
  
   }
  
}


arr_all <- array(NA,c(70,13))

count =1 

colnames(arr_all) <- c("Series_ID", "n_observations", "type", "period", "MAPE_ARIMA", "MASE_ARIMA", "MASE_ARIMA", "model", "MAPE_NAIVE", "MAPE_MEAN", "MAPE_SNAIVE", "Trend","Seasonality")

for (nameId in arr_ids) {

  y_training <- M3[[nameId]]$x  
  y_test <- M3[[nameId]]$xx
  
  #Standard data
  
  y_id <- M3[[nameId]]$sn
  y_type <- M3[[nameId]]$type
  y_period<- M3[[nameId]]$period
  y_num <- M3[[nameId]]$n
  
  arr_all[count,1] <- y_id
  arr_all[count,2] <- y_num
  arr_all[count,3] <- y_type
  arr_all[count,4] <- y_period
  

  
  #ARIMA fit
  
 arima_fit <- forecast::auto.arima(y_training, method="ML", ic="aic")
  
  fc <- forecast::forecast(arima_fit, h = 8)
  
  acc <- forecast::accuracy(fc, y_test)
  

  
  #Fill errors
  
  arr_all[count,5] <- acc[2,5] #MAPE
  
  arr_all[count,6] <- acc[2,6] #MASE
  
  arr_all[count,7] <-  "not applied " #crossVal_ARIMA not applied to save computation time
  
  
  # Get arima information
  
  a <- arimaorder(arima_fit)
  
  str = " "
  
  for (i in 1:length(a)){
    
   str  = paste(str,as.character(a[[i]]))
  }


  
  arr_all[count,8] <- str

  
  
  fx1 <- forecast::naive(y_training)
  
  arr_all[count,9] <- forecast::accuracy(fx1, y_test)[2,5]
  
  fx2 <- forecast::meanf(y_training)
  
  arr_all[count,10] <- forecast::accuracy(fx2, y_test)[2,5]
  
  try(
  fx3 <- forecast::snaive(y_training))
  try(arr_all[count,11] <- forecast::accuracy(fx3, y_test)[2,5])
  

  
 
  #Trend
  
  ac <- forecast::mstl(y_training) 


  value = ac[[1,2]] - ac[[length(y_training),2]] 
  
    
  arr_all[count,12] <- value
  
  
  arr_all[count,13] <- forecast::findfrequency(y_training)
  
  
  
  count = count + 1
}
```



```{r examination 1 - a, echo=FALSE}


arr_tsb <- arr_all %>% as_tibble() 

arr_tsb

```