tesla-stock-prediction.Rmd

---
title: "Tesla Stock Prediction"
author: "G ADITYA KUMAR"
date: "29/9/2023"
output: ioslides_presentation
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
    echo = FALSE,
    warning = FALSE,
    message = FALSE
)
options(knitr.table.format = "html") 
```

```{r libraries}
# plotting
library(ggplot2)

# table format
library(kableExtra)

# forecasting
library(quantmod)
library(forecast)
library(tseries)

theme_set(theme_classic() + theme(
    axis.text = element_text(size = 12),
    axis.title = element_text(size = 14, face = "bold")
))
```


## Description

In this project, I will be using R to predict the TSLA stock.

I will use the ARIMA modelling, more specifically, auto ARIMA to predict the stock 
rise. 

(Disclaimer) This is presentation is not to be taken as an investment advice. 


## The Tesla Stock Data

The data will be is from [Yahoo! Finance](https://finance.yahoo.com/).

Here's a peek at the data

```{r results="hide"}
# specify to and from dates
from.dat <- as.Date("09/27/21", format="%m/%d/%y")
to.dat <- as.Date("09/27/23", format="%m/%d/%y")

# get data for AAPL from Google Finance for the specified dates
getSymbols("TSLA", src="yahoo", from = from.dat, to = to.dat, auto.assign=TRUE)
```


```{r }
head(TSLA) %>%
      kbl() %>%
      kable_classic(full_width = F, html_font = "Cambria")
```

## Plot of TSLA.close stock

```{r}
mTSLA <- to.monthly(TSLA)

# extract the closing price and convert it to yearly time series (12 observations per year)
ts <- ts(Cl(mTSLA))

# plot time series 
plot(ts)

# add the moving average in red
lines(ma(ts,order=3),col="red")
```


## Prediction from ARIMA Model

```{r}
modFit <- auto.arima(ts, lambda = "auto")
price_forecast <- forecast(modFit, h=30)
plot(price_forecast)
```

From our prediction, it shows there was little possiblity our ARIMA model could ever predict the huge stock rise of tesla.