Stock Price Forecasting: Apple Inc. (AAPL)”

library(quantmod)

## Warning: package 'quantmod' was built under R version 4.4.3

## Loading required package: xts

## Warning: package 'xts' was built under R version 4.4.3

## Loading required package: zoo

## Warning: package 'zoo' was built under R version 4.4.3

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

## Loading required package: TTR

## Warning: package 'TTR' was built under R version 4.4.3

## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo

getSymbols("AAPL", from = "2015-01-01", to = Sys.Date(), src = "yahoo")

## [1] "AAPL"

head(AAPL)

##            AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume AAPL.Adjusted
## 2015-01-02   27.8475   27.8600  26.8375    27.3325   212818400      24.28858
## 2015-01-05   27.0725   27.1625  26.3525    26.5625   257142000      23.60433
## 2015-01-06   26.6350   26.8575  26.1575    26.5650   263188400      23.60656
## 2015-01-07   26.8000   27.0500  26.6750    26.9375   160423600      23.93757
## 2015-01-08   27.3075   28.0375  27.1750    27.9725   237458000      24.85731
## 2015-01-09   28.1675   28.3125  27.5525    28.0025   214798000      24.88396

apple_prices <- AAPL[, "AAPL.Adjusted"]

plot(apple_prices, main = "Apple Adjusted Closing Price")

library(tseries)

## Warning: package 'tseries' was built under R version 4.4.3

adf.test(na.omit(as.numeric(apple_prices)))

## 
##  Augmented Dickey-Fuller Test
## 
## data:  na.omit(as.numeric(apple_prices))
## Dickey-Fuller = -2.8219, Lag order = 13, p-value = 0.2304
## alternative hypothesis: stationary

apple_diff <- diff(apple_prices, lag = 1)
plot(apple_diff, main = "First Difference of Apple Prices")

library(forecast)

## Warning: package 'forecast' was built under R version 4.4.3

fit <- auto.arima(apple_prices)
summary(fit)

## Series: apple_prices 
## ARIMA(0,1,0) with drift 
## 
## Coefficients:
##        drift
##       0.0671
## s.e.  0.0426
## 
## sigma^2 = 4.796:  log likelihood = -5806.05
## AIC=11616.11   AICc=11616.11   BIC=11627.86
## 
## Training set error measures:
##                        ME    RMSE      MAE         MPE     MAPE      MASE
## Training set 9.185247e-06 2.18906 1.290308 -0.05802689 1.274627 0.9987194
##                     ACF1
## Training set -0.00497109

forecast_apple <- forecast(fit, h = 30)
library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.4.3

library(forecast)

autoplot(forecast_apple) +
  labs(
    title = "30-Day Forecast of Apple Stock Price",
    x = "Date",
    y = "Adjusted Close Price (USD)"
  ) +
  scale_x_continuous(
    labels = function(x) as.Date(x, origin = "1970-01-01"), 
    breaks = pretty(time(forecast_apple$mean), n = 10)
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.