readd(data_HCM) %>%
plot_time_series(date, value, .interactive = interactive)
readd(splits_HCM) %>%
tk_time_series_cv_plan() %>%
plot_time_series_cv_plan(date, value, .interactive = FALSE)
readd(models_tbl_HCM)
#> # Modeltime Table
#> # A tibble: 4 x 3
#> .model_id .model .model_desc
#> <int> <list> <chr>
#> 1 1 <fit[+]> ARIMA(0,1,0) WITH DRIFT
#> 2 2 <fit[+]> ARIMA(0,1,0) WITH DRIFT W/ XGBOOST ERRORS
#> 3 3 <fit[+]> ETS(M,AD,M)
#> 4 4 <fit[+]> PROPHET
readd(calibration_tbl_HCM)
#> # 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) WITH DRIFT Test <tibble [59 x 4]>
#> 2 2 <fit[+]> ARIMA(0,1,0) WITH DRIFT 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_HCM) %>%
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_HCM)$`_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) WITH DRIFT Test 3.4 7.62 3.84 7.99 4.02 0.55
#> 2 2 ARIMA(0,1,0) WITH DRIFT W/ XGBOOST ERRORS Test 3.16 7.14 3.57 7.42 3.67 0.55
#> 3 3 ETS(M,AD,M) Test 4.79 10.7 5.41 11.5 5.62 0.01
#> 4 4 PROPHET Test 4.76 11.5 5.37 10.7 5.31 0.55
readd(two_week_fc_HCM)
#> # A tibble: 5 x 6
#> .ticker .index .value .low .high .model_desc
#> <chr> <date> <dbl> <dbl> <dbl> <chr>
#> 1 HCM 2022-01-03 46.2 40.1 52.3 ARIMA(0,1,0) WITH DRIFT W/ XGBOOST ERRORS
#> 2 HCM 2022-01-04 46.3 40.2 52.3 ARIMA(0,1,0) WITH DRIFT W/ XGBOOST ERRORS
#> 3 HCM 2022-01-05 46.3 40.3 52.4 ARIMA(0,1,0) WITH DRIFT W/ XGBOOST ERRORS
#> 4 HCM 2022-01-06 46.4 40.3 52.5 ARIMA(0,1,0) WITH DRIFT W/ XGBOOST ERRORS
#> 5 HCM 2022-01-07 46.4 40.4 52.5 ARIMA(0,1,0) WITH DRIFT W/ XGBOOST ERRORS
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