get_forecasts() extracts the true out-of-sample forecasts from an object previously fitted with get_models().
get_forecasts(.tbl, ...) # S3 method for default get_forecasts(.tbl, ...) # S3 method for tbl_df get_forecasts(.tbl, ...)
| .tbl | A tidy |
|---|---|
| ... | Additional parameters to be passed to |
A tidy tibble.
library(YahooTickers) library(dplyr) #> #> Attaching package: ‘dplyr’ #> The following objects are masked from ‘package:stats’: #> #> filter, lag #> The following objects are masked from ‘package:base’: #> #> intersect, setdiff, setequal, union library(forecast) # Download and forecast time series using the "auto.arima" # function from the forecast package get_tickers(dow) %>% slice(1:2) %>% get_stocks(., periodicity = "monthly") %>% get_returns(., tickers, log, TRUE, adjusted) %>% get_models(., tickers, adjusted, 100, 1, FALSE, auto.arima, seasonal = FALSE, stationary = TRUE) %>% get_forecasts(.) # h = 1 because .assess = 1 #> # A tibble: 86 × 8 #> date tickers adjusted point_forecast lo.80 lo.95 hi.80 hi.95 #> <date> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 2018-06-01 MMM 0.00423 0.0110 -0.0514 -0.0844 0.0734 0.106 #> 2 2018-07-01 MMM 0.0763 0.0111 -0.0513 -0.0843 0.0735 0.106 #> 3 2018-08-01 MMM -0.00662 0.0114 -0.0514 -0.0846 0.0741 0.107 #> 4 2018-09-01 MMM 0.00568 0.0107 -0.0518 -0.0848 0.0732 0.106 #> 5 2018-10-01 MMM -0.102 0.0119 -0.0486 -0.0806 0.0723 0.104 #> 6 2018-11-01 MMM 0.0888 0.0109 -0.0513 -0.0842 0.0730 0.106 #> 7 2018-12-01 MMM -0.0805 0.0109 -0.0514 -0.0844 0.0733 0.106 #> 8 2019-01-01 MMM 0.0500 0.0110 -0.0512 -0.0842 0.0732 0.106 #> 9 2019-02-01 MMM 0.0348 0.0104 -0.0508 -0.0832 0.0717 0.104 #> 10 2019-03-01 MMM 0.00877 0.0111 -0.0500 -0.0824 0.0722 0.105 #> # … with 76 more rows