I would like to share my analysis in order to have guidance on how to improve the time series results.

Here you will find a table comparing real values vs forecasted ones.

[![Table][1]][1]

Below you will find the code that generated that output (in R).

    # Load required libraries
    library(forecast)
    library(lubridate)
    library(tidyverse)
    library(scales)
    library(ggfortify)
    
    # Load dataset
    emea <- read.csv(file="C:/Users/nsoria/Google Drive/Trabajo/AMS Globales/812_Finanzas.csv", header=TRUE, sep=';', dec=",")
    
    # Create time series object
    ts_fin <- ts(emea$Valor, deltat = 1/24, start = c(2015, 1))
    
    # Pull out the seasonal, trend, and irregular components from the time series 
    model <- stl(ts_fin, s.window = "periodic")
    
    # Predict the next 3 bi weeks of tickets
    pred <- forecast(model, h = 5)
    
    # Round values to better accuracy
    pred$mean <- round(pred$mean)
    pred$upper <- round(pred$upper)
    pred$lower <- round(pred$lower)

I have added in this link the dataset which is used [here][2].

My main concern would be how to improve accuracy for forecasted results.
I am looking into a possibility to add a machine learning algorithm but I am open to suggestions.

Thanks in advance.

Edit 24/01:
As suggested, you will find the actual results for the prediction and the different outputs.

[![Real Values][3]][3]
[![Auto Arima Forecast][4]][4]
[![ETS Forecast][5]][5]
[![NNar Forecast][6]][6]


  [1]: https://i.sstatic.net/pFqAl.png
  [2]: https://drive.google.com/file/d/1OXoMMScRnXiJn7lBtUdb33lHQscKv4UL/view?usp=sharing
  [3]: https://i.sstatic.net/FzVJ7.jpg
  [4]: https://i.sstatic.net/oOEDs.jpg
  [5]: https://i.sstatic.net/QyTBf.jpg
  [6]: https://i.sstatic.net/ThwpV.jpg