readd(data_HBC) %>%
plot_time_series(date, value, .interactive = interactive)
readd(splits_HBC) %>%
tk_time_series_cv_plan() %>%
plot_time_series_cv_plan(date, value, .interactive = FALSE)
readd(models_tbl_HBC)
#> # Modeltime Table
#> # A tibble: 4 x 3
#> .model_id .model .model_desc
#> <int> <list> <chr>
#> 1 1 <fit[+]> ARIMA(0,1,0)
#> 2 2 <fit[+]> ARIMA(0,1,0) W/ XGBOOST ERRORS
#> 3 3 <fit[+]> ETS(M,AD,M)
#> 4 4 <fit[+]> PROPHET
readd(calibration_tbl_HBC)
#> # Modeltime Table
#> # A tibble: 4 x 5
#> .model_id .model .model_desc .type .calibration_data
#> <int> <list> <chr> <chr> <list>
#> 1 1 <fit[+]> ARIMA(0,1,0) Test <tibble [59 x 4]>
#> 2 2 <fit[+]> ARIMA(0,1,0) W/ XGBOOST ERRORS Test <tibble [59 x 4]>
#> 3 3 <fit[+]> ETS(M,AD,M) Test <tibble [59 x 4]>
#> 4 4 <fit[+]> PROPHET Test <tibble [59 x 4]>
readd(forecast_tbl_HBC) %>%
plot_modeltime_forecast(.legend_max_width = 25,
.interactive = interactive)
#> Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning -Inf
readd(accuracy_tbl_HBC)$`_data`
#> # A tibble: 4 x 9
#> .model_id .model_desc .type mae mape mase smape rmse rsq
#> <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 ARIMA(0,1,0) Test 7 27.5 9.49 33.4 8.14 NA
#> 2 2 ARIMA(0,1,0) W/ XGBOOST ERRORS Test 6.69 26.3 9.07 31.6 7.8 0
#> 3 3 ETS(M,AD,M) Test 6.1 24.0 8.26 28.3 7.12 0.76
#> 4 4 PROPHET Test 9.35 37.7 12.7 48.1 10.3 0.78
readd(two_week_fc_HBC)
#> # A tibble: 5 x 6
#> .ticker .index .value .low .high .model_desc
#> <chr> <date> <dbl> <dbl> <dbl> <chr>
#> 1 HBC 2022-01-03 30.3 18.6 42.1 ETS(M,AD,M)
#> 2 HBC 2022-01-04 30.7 18.9 42.4 ETS(M,AD,M)
#> 3 HBC 2022-01-05 30.9 19.1 42.7 ETS(M,AD,M)
#> 4 HBC 2022-01-06 31.0 19.2 42.7 ETS(M,AD,M)
#> 5 HBC 2022-01-07 31.0 19.2 42.7 ETS(M,AD,M)
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