readd(data_DHG) %>%
plot_time_series(date, value, .interactive = interactive)
readd(splits_DHG) %>%
tk_time_series_cv_plan() %>%
plot_time_series_cv_plan(date, value, .interactive = FALSE)
readd(models_tbl_DHG)
#> # Modeltime Table
#> # A tibble: 4 x 3
#> .model_id .model .model_desc
#> <int> <list> <chr>
#> 1 1 <fit[+]> ARIMA(0,1,0)(1,0,0)[5]
#> 2 2 <fit[+]> ARIMA(0,1,0) W/ XGBOOST ERRORS
#> 3 3 <fit[+]> ETS(M,AD,M)
#> 4 4 <fit[+]> PROPHET
readd(calibration_tbl_DHG)
#> # 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)(1,0,0)[5] 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_DHG) %>%
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_DHG)$`_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)(1,0,0)[5] Test 10.0 8.26 5.27 9.08 15.2 0.06
#> 2 2 ARIMA(0,1,0) W/ XGBOOST ERRORS Test 9.78 8.06 5.14 8.84 15.0 NA
#> 3 3 ETS(M,AD,M) Test 10.1 8.37 5.33 9.2 15.4 0
#> 4 4 PROPHET Test 8.15 6.7 4.29 7.25 12.8 0.61
readd(two_week_fc_DHG)
#> # A tibble: 5 x 6
#> .ticker .index .value .low .high .model_desc
#> <chr> <date> <dbl> <dbl> <dbl> <chr>
#> 1 DHG 2022-01-03 116. 94.9 137. PROPHET
#> 2 DHG 2022-01-04 116. 95.2 137. PROPHET
#> 3 DHG 2022-01-05 116. 95.2 137. PROPHET
#> 4 DHG 2022-01-06 116. 95.4 138. PROPHET
#> 5 DHG 2022-01-07 116. 95.3 138. PROPHET
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.