readd(data_SHS) %>%
plot_time_series(date, value, .interactive = interactive)
readd(splits_SHS) %>%
tk_time_series_cv_plan() %>%
plot_time_series_cv_plan(date, value, .interactive = FALSE)
readd(models_tbl_SHS)
#> # 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_SHS)
#> # 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_SHS) %>%
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_SHS)$`_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 6.16 12.4 5.82 13.6 7.43 0.64
#> 2 2 ARIMA(0,1,0) WITH DRIFT W/ XGBOOST ERRORS Test 5.9 12.0 5.58 13.0 7.06 0.64
#> 3 3 ETS(M,AD,M) Test 8.29 16.7 7.85 18.9 9.93 0
#> 4 4 PROPHET Test 3.34 7.12 3.16 7.29 4.21 0.64
readd(two_week_fc_SHS)
#> # A tibble: 5 x 6
#> .ticker .index .value .low .high .model_desc
#> <chr> <date> <dbl> <dbl> <dbl> <chr>
#> 1 SHS 2022-01-03 51.5 44.5 58.4 PROPHET
#> 2 SHS 2022-01-04 51.6 44.6 58.5 PROPHET
#> 3 SHS 2022-01-05 51.7 44.8 58.7 PROPHET
#> 4 SHS 2022-01-06 51.9 45.0 58.9 PROPHET
#> 5 SHS 2022-01-07 52.0 45.1 59.0 PROPHET
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