Final answer:
Produce time series plots using R, fit a time series regression model, exponential smoothing, and ARIMA or SARIMA models, and show summaries/reports for each using appropriate functions (e.g., `lm`, `ets`, `arima`, `auto.arima`, `summary`).
Step-by-step explanation:
To accomplish the tasks outlined:
1. Produce Time Series Plots:
Utilize your time series data and employ a suitable plotting function in R (such as `plot` or `ggplot2`) to create time series plots, providing a visual representation of the data over time.
2. Fit Suitable Models:
a. Time Series Regression Model:
Utilize the `lm` function in R for time series regression, incorporating time-related variables if necessary.
b. Exponential Smoothing Method:
Use the `ets` function in R for exponential smoothing, selecting appropriate parameters for trend, seasonality, and error.
c. ARIMA or SARIMA Model:
Utilize the `arima` or `auto.arima` function in R for fitting an ARIMA or SARIMA model, respectively. Adjust model parameters based on diagnostic checks.
3. Show Model Summaries/Reports:
For each model/method:
a. Time Series Regression Model:
Display the summary using `summary` on the model fitted with `lm`.
b. Exponential Smoothing Method:
Obtain a summary or report from the `summary` function applied to the `ets` model.
c. ARIMA or SARIMA Model:
Display a summary or diagnostic report using functions like `summary` or `checkresiduals` on the fitted ARIMA or SARIMA model.
Ensure to interpret the results and validate the chosen models through diagnostics to ensure they adequately capture the underlying patterns in the time series data.