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library(fpp3)

── Attaching packages ──────────────────────────────────────────── fpp3 1.0.3 ──

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✔ dplyr       1.2.1     ✔ tsibbledata 0.4.1
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✔ lubridate   1.9.5     ✔ feasts      0.5.0
✔ ggplot2     4.0.3     ✔ fable       0.5.0

── Conflicts ───────────────────────────────────────────────── fpp3_conflicts ──
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library(tidyquant)

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

── Attaching core tidyquant packages ─────────────────────── tidyquant 1.0.12 ──
✔ PerformanceAnalytics 2.1.0      ✔ TTR                  0.24.4
✔ quantmod             0.4.29     ✔ xts                  0.14.2
── Conflicts ────────────────────────────────────────── tidyquant_conflicts() ──
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ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Attaching package: 'tidyquant'


The following object is masked from 'package:fable':

    VAR

library(dplyr)

# Download Apple stock data
df_daily <- tq_get(
  "AAPL",
  get = "stock.prices",
  from = "2020-01-01"
)

# Convert daily data to monthly time series
stock_monthly <- df_daily %>%
  mutate(month = yearmonth(date)) %>%
  group_by(month) %>%
  summarise(price = mean(adjusted)) %>%
  as_tsibble(index = month)

# Split into training (80%) and testing (20%)
n <- nrow(stock_monthly)

train <- stock_monthly[1:floor(0.8 * n), ]
test <- stock_monthly[(floor(0.8 * n) + 1):n, ]

# Fit the three forecasting models
models <- train %>%
  model(
    Naive = NAIVE(price),
    SNaive = SNAIVE(price),
    Drift = RW(price ~ drift())
  )

# Forecast on the test data
forecast_stock <- models %>%
  forecast(h = nrow(test))

# Plot forecasts vs actual values
autoplot(forecast_stock, data = train) +
  autolayer(test, price) +
  labs(
    title = "Apple Stock Price Forecast",
    x = "Date",
    y = "Adjusted Closing Price"
  )

# Compare forecasting accuracy
accuracy(forecast_stock, test)

# A tibble: 3 × 10
  .model .type    ME  RMSE   MAE   MPE  MAPE  MASE RMSSE  ACF1
  <chr>  <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Drift  Test   11.2  28.5  24.4  3.01  9.46   NaN   NaN 0.681
2 Naive  Test   31.3  47.8  40.4 10.5  15.0    NaN   NaN 0.730
3 SNaive Test   40.6  55.2  42.7 14.7  15.7    NaN   NaN 0.769

# Rank models by RMSE
accuracy(forecast_stock, test) %>%
  arrange(RMSE)

# A tibble: 3 × 10
  .model .type    ME  RMSE   MAE   MPE  MAPE  MASE RMSSE  ACF1
  <chr>  <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Drift  Test   11.2  28.5  24.4  3.01  9.46   NaN   NaN 0.681
2 Naive  Test   31.3  47.8  40.4 10.5  15.0    NaN   NaN 0.730
3 SNaive Test   40.6  55.2  42.7 14.7  15.7    NaN   NaN 0.769

cat("\nRecommendation:\n")

Recommendation:

cat("Choose the model with the lowest RMSE.\n")

Choose the model with the lowest RMSE.

cat("If the Drift model has the lowest RMSE and predicts an upward trend, Buy/Hold the stock.\n")

If the Drift model has the lowest RMSE and predicts an upward trend, Buy/Hold the stock.

cat("If the forecast trends downward, Sell or Avoid buying.\n")

If the forecast trends downward, Sell or Avoid buying.