Time Series Analysis, AR and MA models

General
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1

Hello everyone. I have this time series data "Flu_data_2022_2023" which is a weekly rate of flu cases in the 2022-2023 seasons. Below is my code for the plot. if i am not sure to tell whether it is stationary or non stationary from the plots. what other things or can i use to check for the stationarity. what codes can i use?

Also, if i want to fit AR model and MA model for the data and i don't want to rely on the acf plot and the pacf plot to determine the order for the MA and the AR model respectively. what packages can i use to fit the model with the appropriate order for my models.

  1. What best Time Series book will you recommend for someone new into Time Series like myself?
Flu_data_2022_2023 <- Flu_data %>% filter(Season == "2022-23")


ggplot(data = Flu_data_2022_2023, aes(x = as.Date(Week.Ending.Date), y = Weekly.Rate)) +
  geom_line(color = "violet") +
  labs(title = "Weekly Rate for the 2022-2023 Season",
       x = "MMWR Week",
       y = "Weekly Rate") +
#  scale_x_continuous(breaks = seq(min(as.numeric(Flu_data_2022_2023$MMWR.Week)), max(as.numeric(Flu_data_2022_2023$MMWR.Week)), by = 2)) +
  theme(plot.title = element_text(hjust = 0.5))
2

Hi, if after an answer to your question, please provide a reproducible example.

For resources, have a look at:

Forecasting: Principles and Practice (3rd ed) (otexts.com)

Otherwise:

A Very Short Course on Time Series Analysis (bookdown.org)