ARIMA Issues in RStudio - ARIMA for Stocks - r

Question

This is my first post here on this platform. I'm an student in Business Administration so please have mercy with my nooby questions.
I'm currently creating ARIMA Models for some Stocks respectively their closing prices. However, when plotting the forecasts, all I get is a straight line with a little bit of drift. But that's it. I don't get any clear patterns for example, no ups and downs in the forecast, just straight line with drift.
I'm not sure if I did any kind of mistake maybe.
install.packages(quantmod)
install.packages(tseries)
install.packages(timeSeries)
install.packages(forecast)
install.packages(MASS)
install.packages(ggplot2)
install.packages(zoo)
install.packages(xts)
library(quantmod)
library(tseries)
library(timeSeries)
library(forecast)
library(MASS)
library(ggplot2)
library(zoo)
library(xts)
# load data
energy = getSymbols(Symbols = "XLES.L", auto.assign = F, from = "2015-01-01", to = "2020-01-01")
# remove NAs
energy <- na.omit(energy$XLES.L.Close)
plot(energy)
# create TS
ts <- ts(energy, start = c(2015,01), frequency = 252)
plot(ts) #does not seem stationary
# check for stationarity
adf.test(ts) # --> not stationariy, differencing required
#Create Arima Model
arima <- auto.arima(ts, d = 1)
arima
# Create Forecast (Out-Of-Sample for 20days/1month)
forecast_energy <- forecast(arima, h = 20)
plot(forecast_energy)
plot(forecast_energy, include = 50)
My questions are:
Why is it a straight line?
Is it necessary to create a Time Series with the ts-function since the data imported is already in a ts (or is it not?)
Is this correct what I did?
HERE THE PLOTS:
HERE THE PRINT
> print(arima)
Series: ts
ARIMA(2,1,0)
Coefficients:
ar1 ar2
0.0125 -0.0502
s.e. 0.0283 0.0283
sigma^2 estimated as 20.19: log likelihood=-3682.99
AIC=7371.98 AICc=7372 BIC=7387.4
Can someone please help me :)
Best regards
Noob

Answer 1

An example of simple signal, which is able to break auto arima
library(forecast)
set.seed(1)
mynoise <- rnorm(252*5,0,sd = 100) # high short term noise, non integrated
mytrend <- 1:(252*5) # long term trend
mysignal <- mynoise+mytrend
library(forecast)
mymodel <- auto.arima(mysignal)
plot(forecast(mymodel,50))
the difference of the signal is u=1+e-lag(e) and lag(u)=1+lag(e)-lag2(e)
let epsilon be e-lag(e) it is an ar1 with epsilon=-lag(epsilon)+e
So the process is likely to be seen as a stationnary 011, with 1 non very significative, and then auto arima estimates an arima(0,1,1) with the moving average term around -1.
Which is not a total fail : it's decent for short terms predictions, but it makes silly long term predictions.

Answer 2

You are getting forecast as a straight line because your model is not able to find and seasonality in data, when this happen the model simply take average of your historical data and generate forecast, that is why you are getting straight line.
It is very difficult for a model to forecast accurately with out any good seasonality and trend present in historical data.