For the COVID19 data from the United States develop the best forecasting model and produce a forecast for the next 30 days. Note that the data is recorded daily. please do the following with R STUDIO.

See Section 9.11 Exercises 10 for an example.

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library(pacman) p_load(fpp3, tidyverse, COVID19)

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covid_data <- covid19(c("United States"))

trying URL 'https://storage.covid19datahub.io/level/1.csv.gz' Content type 'application/octet-stream' length 3434009 bytes (3.3 MB) ================================================== downloaded 3.3 MB

We have invested a lot of time and effort in creating COVID-19 Data Hub, please cite the following when using it: To cite package 'COVID19' in publications use: Guidotti, E., Ardia, D., (2020), "COVID-19 Data Hub", Journal of Open Source Software 5(51):2376, doi: 10.21105/joss.02376. A BibTeX entry for LaTeX users is @Article{, title = {COVID-19 Data Hub}, year = {2020}, doi = {10.21105/joss.02376}, author = {Emanuele Guidotti and David Ardia}, journal = {Journal of Open Source Software}, volume = {5}, number = {51}, pages = {2376}, } To hide this message use 'verbose = FALSE'.

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covid_data <- covid_data %>% as_tsibble(key = "id", index = "date")

1. Produce an STL decomposition of the data and describe the trend and seasonality.

Answer:

Summarize your answer to the question here. All code and comments should be below and your written answer above.

Code and Comments:

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covid_data %>% autoplot(confirmed)