if you're doing time series forecasting then you should likely look at the forecast package from RJ Hyndman.
His book, Forecasting Principles and Practice is freely available online: https://otexts.org/fpp2/
Here's an example of building an example time series model and associated plots:
library(forecast)
library(fpp2)
#> Loading required package: ggplot2
#> Loading required package: fma
#> Loading required package: expsmooth
a10 %>%
BoxCox.lambda() ->
lambda
a10 %>%
BoxCox(lambda) %>%
auto.arima() ->
fit
print(fit)
#> Series: .
#> ARIMA(3,0,3)(0,1,1)[12] with drift
#>
#> Coefficients:
#> ar1 ar2 ar3 ma1 ma2 ma3 sma1 drift
#> -0.0607 -0.0007 0.7459 0.0751 0.2973 -0.4682 -0.8276 0.0126
#> s.e. 0.1207 0.1087 0.0966 0.1440 0.1246 0.1357 0.0663 0.0003
#>
#> sigma^2 estimated as 0.006168: log likelihood=213.52
#> AIC=-409.03 AICc=-408.04 BIC=-379.72
checkresiduals(fit, plot=TRUE)

#>
#> Ljung-Box test
#>
#> data: Residuals from ARIMA(3,0,3)(0,1,1)[12] with drift
#> Q* = 20.172, df = 16, p-value = 0.2126
#>
#> Model df: 8. Total lags used: 24
fit %>% forecast() %>% autoplot()

Created on 2018-06-26 by the reprex package (v0.2.0).