Getting started
introduction.RmdIntroduction
This vignette briefly outlines the functionality of
EpiSoon. To get started load the required packages.
- Load the package (
bstsfor models,ggplot2for plotting, andcowplotfor theming)
Forecast Rts, score and plot
- We use an example dataframe built into the package but this could be replaced with your own data.
EpiSoon::example_obs_rts
#> rt date
#> 1 2.490547 2020-03-01
#> 2 2.442588 2020-03-02
#> 3 2.402473 2020-03-03
#> 4 2.335572 2020-03-04
#> 5 2.266551 2020-03-05
#> 6 2.192293 2020-03-06
#> 7 2.146429 2020-03-07
#> 8 2.104371 2020-03-08
#> 9 2.059281 2020-03-09
#> 10 2.027134 2020-03-10
#> 11 2.014678 2020-03-11
#> 12 1.998946 2020-03-12
#> 13 1.968350 2020-03-13
#> 14 1.947376 2020-03-14
#> 15 1.906984 2020-03-15
#> 16 1.812842 2020-03-16
#> 17 1.718532 2020-03-17
#> 18 1.665646 2020-03-18
#> 19 1.639927 2020-03-19
#> 20 1.633795 2020-03-20
#> 21 1.682025 2020-03-21
#> 22 1.561653 2020-03-22- Fit a
bstsmodel and produce a Rt forecast. Any appropriately wrapped model can be used (seebsts_modelandfable_modelfor an examples).
rt_forecast <- forecast_rt(EpiSoon::example_obs_rts[1:10, ],
model = function(...) {
EpiSoon::bsts_model(model = function(ss, y) {
bsts::AddAutoAr(ss, y = y, lags = 10)
}, ...)
},
horizon = 21, samples = 10
)
rt_forecast
#> # A tibble: 210 × 4
#> sample date rt horizon
#> <int> <date> <dbl> <int>
#> 1 1 2020-03-11 1.95 1
#> 2 2 2020-03-11 1.87 1
#> 3 3 2020-03-11 2.05 1
#> 4 4 2020-03-11 1.87 1
#> 5 5 2020-03-11 1.96 1
#> 6 6 2020-03-11 2.20 1
#> 7 7 2020-03-11 1.99 1
#> 8 8 2020-03-11 1.81 1
#> 9 9 2020-03-11 1.92 1
#> 10 10 2020-03-11 1.99 1
#> # ℹ 200 more rows- Score the forecast
rt_scores <- score_forecast(rt_forecast, EpiSoon::example_obs_rts)
rt_scores
#> date horizon bias dss crps overprediction
#> <Date> <int> <num> <num> <num> <num>
#> 1: 2020-03-11 1 -0.6 -4.26687941 0.04192063 0
#> 2: 2020-03-12 2 -0.6 -3.02850406 0.07785852 0
#> 3: 2020-03-13 3 -0.6 -2.30966555 0.10380478 0
#> 4: 2020-03-14 4 -0.6 -1.77641556 0.10445634 0
#> 5: 2020-03-15 5 -0.4 -1.28192670 0.10950395 0
#> 6: 2020-03-16 6 -0.2 -0.88634354 0.13719163 0
#> 7: 2020-03-17 7 -0.2 -0.53970598 0.11916345 0
#> 8: 2020-03-18 8 0.0 -0.26053507 0.11733866 0
#> 9: 2020-03-19 9 0.0 0.06612982 0.11786249 0
#> 10: 2020-03-20 10 -0.4 0.38330523 0.15899679 0
#> 11: 2020-03-21 11 -0.4 0.59570852 0.19924557 0
#> 12: 2020-03-22 12 -0.2 0.83983548 0.17020136 0
#> underprediction dispersion log_score mad ae_median se_mean
#> <num> <num> <num> <num> <num> <num>
#> 1: 0.021262509 0.02065812 -1.09398992 0.08995963 0.056398410 0.0027938743
#> 2: 0.043143145 0.03471537 -0.23501059 0.13332185 0.091215633 0.0073746656
#> 3: 0.061318072 0.04248671 0.07029008 0.14297986 0.132407802 0.0098568424
#> 4: 0.027088236 0.07736810 -0.13236855 0.22651493 0.146235082 0.0099815360
#> 5: 0.021912568 0.08759138 0.02974890 0.33013908 0.117221454 0.0052856962
#> 6: 0.019146931 0.11804470 0.30112305 0.43517231 0.148961189 0.0003712525
#> 7: 0.001745678 0.11741777 0.23579815 0.45209167 0.022941969 0.0031058838
#> 8: 0.000000000 0.11733866 0.17107458 0.39142197 0.042827480 0.0106513393
#> 9: 0.000000000 0.11786249 0.08867359 0.39513915 0.006737829 0.0232891476
#> 10: 0.032783719 0.12621307 0.21859328 0.41619226 0.130263976 0.0125321098
#> 11: 0.047152021 0.15209355 0.50025344 0.56261141 0.211715315 0.0117916476
#> 12: 0.013714110 0.15648725 0.42448296 0.52268099 0.094946098 0.0861271392- Summarise the forecast scores
summarise_scores(rt_scores)
#> # A tibble: 10 × 8
#> score bottom lower median mean upper top sd
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 ae_median 0.0112 0.0530 0.106 0.100 0.136 0.194 0.0597
#> 2 bias -0.6 -0.6 -0.4 -0.35 -0.2 0 0.228
#> 3 crps 0.0518 0.104 0.118 0.121 0.143 0.191 0.0417
#> 4 dispersion 0.0245 0.0686 0.117 0.0974 0.120 0.155 0.0450
#> 5 dss -3.93 -1.91 -0.713 -1.04 0.145 0.773 1.56
#> 6 log_score -0.858 -0.0108 0.130 0.0482 0.252 0.479 0.417
#> 7 mad 0.102 0.206 0.393 0.342 0.439 0.552 0.158
#> 8 overprediction 0 0 0 0 0 0 0
#> 9 se_mean 0.00104 0.00474 0.00992 0.0153 0.0120 0.0688 0.0231
#> 10 underprediction 0 0.0107 0.0216 0.0241 0.0354 0.0574 0.0195- Summarise the forecast
summarised_rt_forecast <- summarise_forecast(rt_forecast)
summarised_rt_forecast
#> # A tibble: 21 × 9
#> date horizon median mean sd bottom lower upper top
#> <date> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2020-03-11 1 1.96 1.96 0.110 1.81 1.87 1.99 2.20
#> 2 2020-03-12 2 1.91 1.91 0.212 1.62 1.81 1.94 2.37
#> 3 2020-03-13 3 1.84 1.87 0.314 1.44 1.68 1.88 2.60
#> 4 2020-03-14 4 1.80 1.85 0.420 1.37 1.62 1.92 2.86
#> 5 2020-03-15 5 1.79 1.83 0.550 1.16 1.61 2.06 3.17
#> 6 2020-03-16 6 1.66 1.79 0.676 1.07 1.50 2.05 3.47
#> 7 2020-03-17 7 1.70 1.77 0.803 0.899 1.37 1.90 3.82
#> 8 2020-03-18 8 1.62 1.77 0.919 0.805 1.37 1.90 4.17
#> 9 2020-03-19 9 1.63 1.79 1.08 0.707 1.48 1.91 4.66
#> 10 2020-03-20 10 1.50 1.75 1.27 0.491 1.39 1.92 5.15
#> # ℹ 11 more rows- Plot the forecast against observed data
plot_forecast(summarised_rt_forecast, EpiSoon::example_obs_rts)
Forecast cases, score and plot
- Forecasting cases requires the observed cases on which the observed Rt estimates were based
EpiSoon::example_obs_cases
#> # A tibble: 63 × 2
#> cases date
#> <dbl> <date>
#> 1 1 2020-01-20
#> 2 0 2020-01-21
#> 3 1 2020-01-22
#> 4 0 2020-01-23
#> 5 0 2020-01-24
#> 6 0 2020-01-25
#> 7 1 2020-01-26
#> 8 0 2020-01-27
#> 9 0 2020-01-28
#> 10 0 2020-01-29
#> # ℹ 53 more rows- It also requires an assumption to be made about the serial interval (defined using probability distribution).
EpiSoon::example_serial_interval
#> 1 2 3 4 5 6 7 8 9 10 11 12 14
#> 0.00 0.03 0.25 0.17 0.09 0.15 0.13 0.05 0.05 0.03 0.02 0.01 0.01 0.01- Forecast cases (using the case data on which the observed Rt estimates were based)
case_forecast <- forecast_cases(EpiSoon::example_obs_cases, rt_forecast,
serial_interval = EpiSoon::example_serial_interval
)
case_forecast
#> sample date cases horizon
#> <num> <Date> <int> <int>
#> 1: 1 2020-03-11 147 1
#> 2: 1 2020-03-12 177 2
#> 3: 1 2020-03-13 213 3
#> 4: 1 2020-03-14 261 4
#> 5: 1 2020-03-15 314 5
#> ---
#> 206: 10 2020-03-27 507 17
#> 207: 10 2020-03-28 569 18
#> 208: 10 2020-03-29 491 19
#> 209: 10 2020-03-30 610 20
#> 210: 10 2020-03-31 659 21- Score the cases forecast
case_scores <- score_case_forecast(case_forecast, EpiSoon::example_obs_cases)
case_scores
#> date horizon bias dss crps overprediction underprediction
#> <Date> <int> <num> <num> <num> <num> <num>
#> 1: 2020-03-11 1 0.0 4.904574 2.29 0.0 0.000000e+00
#> 2: 2020-03-12 2 -0.4 6.191340 6.27 0.0 1.000000e+00
#> 3: 2020-03-13 3 -0.2 7.810147 7.93 0.0 4.000000e-01
#> 4: 2020-03-14 4 0.0 8.622401 14.57 0.0 -5.329071e-15
#> 5: 2020-03-15 5 0.2 9.532459 18.47 0.8 0.000000e+00
#> 6: 2020-03-16 6 0.6 10.782962 30.23 4.6 0.000000e+00
#> 7: 2020-03-17 7 0.2 11.851515 51.18 8.4 0.000000e+00
#> 8: 2020-03-18 8 0.0 12.777103 57.56 0.0 0.000000e+00
#> 9: 2020-03-19 9 0.0 13.855333 83.82 0.0 0.000000e+00
#> 10: 2020-03-20 10 -0.2 14.969060 116.87 0.0 1.000000e+00
#> 11: 2020-03-21 11 -0.2 16.054158 195.96 0.0 2.130000e+01
#> 12: 2020-03-22 12 0.4 17.322897 304.86 24.4 0.000000e+00
#> dispersion log_score mad ae_median se_mean
#> <num> <num> <num> <num> <num>
#> 1: 2.29 3.042475 5.9304 0.0 1.21
#> 2: 5.27 4.292595 24.4629 4.5 0.25
#> 3: 7.53 4.395320 24.4629 4.0 53.29
#> 4: 14.57 5.014852 40.7715 12.5 8.41
#> 5: 17.67 5.240384 54.8562 9.0 858.49
#> 6: 25.63 5.523580 95.6277 21.5 7140.25
#> 7: 42.78 6.094497 157.1556 51.5 18550.44
#> 8: 57.56 6.241611 208.3053 1.5 36404.64
#> 9: 83.82 6.459608 251.3007 16.5 92842.09
#> 10: 115.87 6.682583 246.1116 19.5 277412.89
#> 11: 174.66 7.022725 335.0676 156.0 685087.29
#> 12: 280.46 7.118067 470.7255 144.5 3870269.29- Summarise the cases scores
summarise_scores(case_scores)
#> # A tibble: 10 × 8
#> score bottom lower median mean upper top sd
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 ae_median 4.12e- 1 4.38 14.5 36.8 2.9 e+1 1.53e+2 5.48e+1
#> 2 bias -3.45e- 1 -0.2 0 0.0333 2 e-1 5.45e-1 2.81e-1
#> 3 crps 3.38e+ 0 12.9 40.7 74.2 9.21e+1 2.75e+2 9.22e+1
#> 4 dispersion 3.11e+ 0 12.8 34.2 69.0 9.18e+1 2.51e+2 8.47e+1
#> 5 dss 5.26e+ 0 8.42 11.3 11.2 1.41e+1 1.70e+1 3.94e+0
#> 6 log_score 3.39e+ 0 4.86 5.81 5.59 6.52e+0 7.09e+0 1.25e+0
#> 7 mad 1.10e+ 1 36.7 126. 160. 2.47e+2 4.33e+2 1.46e+2
#> 8 overprediction 0 0 0 3.18 1.75e+0 2.00e+1 7.17e+0
#> 9 se_mean 5.14e- 1 42.1 12845. 415719. 1.39e+5 2.99e+6 1.11e+6
#> 10 underprediction -3.86e-15 0 0 1.98 5.5 e-1 1.57e+1 6.10e+0- Summarise the cases forecast
summarised_case_forecast <- summarise_case_forecast(case_forecast)
summarised_case_forecast
#> # A tibble: 21 × 9
#> date horizon median mean sd bottom lower upper top
#> <date> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2020-03-11 1 167 166. 12.2 147 163 171 188
#> 2 2020-03-12 2 190. 194. 23.3 169 169 190 240
#> 3 2020-03-13 3 204 215. 51.8 142 180 213 337
#> 4 2020-03-14 4 238. 254. 78.5 160 208 262 445
#> 5 2020-03-15 5 282 302. 120. 177 242 315 610
#> 6 2020-03-16 6 288. 350. 212. 139 272 373 908
#> 7 2020-03-17 7 348. 432. 366. 143 205 395 1424
#> 8 2020-03-18 8 342. 534. 592. 142 142 354 2172
#> 9 2020-03-19 9 383 704. 1024. 102 310 559 3575
#> 10 2020-03-20 10 434. 981. 1788. 94 277 603 6032
#> # ℹ 11 more rows- Plot the forecast against observed case data
plot_forecast(summarised_case_forecast, EpiSoon::example_obs_cases)
Use iterative fitting to explore a forecast
- To explore the quality of a models forecast it can help to
iteratively forecast from each available data point. This is supported
in
EpiSoonusing the following:
it_rt_forecast <- iterative_rt_forecast(EpiSoon::example_obs_rts,
model = function(...) {
EpiSoon::bsts_model(model = function(ss, y) {
bsts::AddAutoAr(ss, y = y, lags = 10)
}, ...)
},
horizon = 7, samples = 10, min_points = 4
)
it_rt_forecast
#> # A tibble: 1,260 × 5
#> forecast_date sample date rt horizon
#> <chr> <int> <date> <dbl> <int>
#> 1 2020-03-05 1 2020-03-06 2.19 1
#> 2 2020-03-05 2 2020-03-06 2.31 1
#> 3 2020-03-05 3 2020-03-06 2.24 1
#> 4 2020-03-05 4 2020-03-06 2.08 1
#> 5 2020-03-05 5 2020-03-06 2.28 1
#> 6 2020-03-05 6 2020-03-06 2.15 1
#> 7 2020-03-05 7 2020-03-06 2.16 1
#> 8 2020-03-05 8 2020-03-06 2.26 1
#> 9 2020-03-05 9 2020-03-06 2.21 1
#> 10 2020-03-05 10 2020-03-06 2.23 1
#> # ℹ 1,250 more rows- We can then iteratively forecast cases using the following:
it_cases_forecast <- iterative_case_forecast(
it_fit_samples = it_rt_forecast,
cases = EpiSoon::example_obs_cases,
serial_interval = EpiSoon::example_serial_interval
)
it_cases_forecast
#> forecast_date sample date cases horizon
#> <char> <num> <Date> <int> <int>
#> 1: 2020-03-05 1 2020-03-06 86 1
#> 2: 2020-03-05 1 2020-03-07 76 2
#> 3: 2020-03-05 1 2020-03-08 82 3
#> 4: 2020-03-05 1 2020-03-09 81 4
#> 5: 2020-03-05 1 2020-03-10 66 5
#> ---
#> 1256: 2020-03-22 10 2020-03-25 669 3
#> 1257: 2020-03-22 10 2020-03-26 588 4
#> 1258: 2020-03-22 10 2020-03-27 672 5
#> 1259: 2020-03-22 10 2020-03-28 584 6
#> 1260: 2020-03-22 10 2020-03-29 621 7- All functionality shown above is also supported for iterative forecasting.
Evaluate a model
In real world use we are likely to want to evaluate a model by
iteratively forecasting Rts and cases, summarising these forecasts,
scoring them and then returning them in a sensible format. These steps
are all contained in the evaluate_model function.
model_eval <- evaluate_model(EpiSoon::example_obs_rts,
EpiSoon::example_obs_cases,
model = function(...) {
EpiSoon::bsts_model(model = function(ss, y) {
bsts::AddAutoAr(ss, y = y, lags = 10)
}, ...)
},
horizon = 21, samples = 10,
serial_interval = EpiSoon::example_serial_interval
)
model_eval
#> $forecast_rts
#> # A tibble: 399 × 10
#> forecast_date date horizon median mean sd bottom lower upper top
#> <chr> <date> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2020-03-04 2020-03-05 1 2.30 2.26 0.182 1.75 2.26 2.31 2.40
#> 2 2020-03-04 2020-03-06 2 2.30 2.21 0.313 1.36 2.17 2.32 2.45
#> 3 2020-03-04 2020-03-07 3 2.27 2.12 0.508 0.715 2.15 2.29 2.48
#> 4 2020-03-04 2020-03-08 4 2.23 2.03 0.716 0.0474 2.05 2.26 2.53
#> 5 2020-03-04 2020-03-09 5 2.17 2.01 0.733 0 2.01 2.29 2.57
#> 6 2020-03-04 2020-03-10 6 2.14 1.98 0.732 0 1.94 2.23 2.60
#> 7 2020-03-04 2020-03-11 7 2.10 1.97 0.740 0 1.93 2.24 2.67
#> 8 2020-03-04 2020-03-12 8 2.05 1.97 0.743 0 1.82 2.18 2.64
#> 9 2020-03-04 2020-03-13 9 2.01 1.94 0.753 0 1.75 2.11 2.68
#> 10 2020-03-04 2020-03-14 10 2.00 1.92 0.763 0 1.69 2.07 2.72
#> # ℹ 389 more rows
#>
#> $rt_scores
#> forecast_date date horizon bias dss crps
#> <char> <Date> <int> <num> <num> <num>
#> 1: 2020-03-04 2020-03-05 1 0.6 -3.5066176 0.02435233
#> 2: 2020-03-04 2020-03-06 2 0.6 -2.4250734 0.06289341
#> 3: 2020-03-04 2020-03-07 3 0.6 -1.4573688 0.07011137
#> 4: 2020-03-04 2020-03-08 4 0.4 -0.7617366 0.09159354
#> 5: 2020-03-04 2020-03-09 5 0.4 -0.7210385 0.10343993
#> ---
#> 167: 2020-03-19 2020-03-21 2 -0.8 -1.1852827 0.08661630
#> 168: 2020-03-19 2020-03-22 3 -0.4 -5.8838246 0.02126621
#> 169: 2020-03-20 2020-03-21 1 -0.6 -5.3199530 0.02384127
#> 170: 2020-03-20 2020-03-22 2 0.0 -3.0472682 0.04651778
#> 171: 2020-03-21 2020-03-22 1 1.0 -3.2176047 0.05631644
#> overprediction underprediction dispersion log_score mad ae_median
#> <num> <num> <num> <num> <num> <num>
#> 1: 0.00671177 0.000000000 0.01764056 -1.7331282 0.05315893 0.03279765
#> 2: 0.02764666 0.000000000 0.03524676 -0.7448557 0.12627796 0.10675989
#> 3: 0.02621642 0.000000000 0.04389495 -0.7759505 0.16299330 0.12006801
#> 4: 0.03394458 0.000000000 0.05764896 -0.4893418 0.18700899 0.12456683
#> 5: 0.03405054 0.000000000 0.06938939 -0.2917767 0.20955012 0.11038333
#> ---
#> 167: 0.00000000 0.078739278 0.00787702 -0.4443722 0.02462206 0.11687595
#> 168: 0.00000000 0.007076893 0.01418932 -1.6517020 0.04878211 0.03965038
#> 169: 0.00000000 0.009532557 0.01430871 -1.6557728 0.05489284 0.03325382
#> 170: 0.00000000 0.000000000 0.04651778 -0.7506340 0.14348333 0.05736374
#> 171: 0.04296477 0.000000000 0.01335166 -1.0835648 0.03210339 0.06150011
#> se_mean
#> <num>
#> 1: 8.510265e-05
#> 2: 3.557970e-04
#> 3: 6.197036e-04
#> 4: 5.027910e-03
#> 5: 2.622510e-03
#> ---
#> 167: 1.175799e-02
#> 168: 7.441686e-04
#> 169: 8.643219e-04
#> 170: 5.933604e-03
#> 171: 6.919049e-03
#>
#> $forecast_cases
#> # A tibble: 171 × 10
#> forecast_date date horizon median mean sd bottom lower upper top
#> <chr> <date> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2020-03-04 2020-03-05 1 66 67.2 8.11 56 63 69 85
#> 2 2020-03-04 2020-03-06 2 87.5 83.7 12.6 50 87 93 93
#> 3 2020-03-04 2020-03-07 3 104 95.7 25.2 30 97 110 120
#> 4 2020-03-04 2020-03-08 4 134. 123. 45.3 1 125 145 159
#> 5 2020-03-04 2020-03-09 5 149 144. 55.2 0 129 154 199
#> 6 2020-03-04 2020-03-10 6 190. 179. 70.4 0 173 219 249
#> 7 2020-03-04 2020-03-11 7 242. 222. 92.1 0 196 266 338
#> 8 2020-03-04 2020-03-12 8 264. 257. 109. 0 260 353 382
#> 9 2020-03-04 2020-03-13 9 336. 317. 151. 0 241 381 544
#> 10 2020-03-04 2020-03-14 10 374. 388. 203. 0 234 385 741
#> # ℹ 161 more rows
#>
#> $case_scores
#> sample forecast_date date horizon bias dss crps
#> <char> <char> <Date> <int> <num> <num> <num>
#> 1: 1 2020-03-04 2020-03-05 1 0.4 4.378420 1.92
#> 2: 1 2020-03-04 2020-03-06 2 0.8 5.761836 10.03
#> 3: 1 2020-03-04 2020-03-07 3 0.6 6.455313 8.53
#> 4: 1 2020-03-04 2020-03-08 4 0.8 7.763714 22.43
#> 5: 1 2020-03-04 2020-03-09 5 0.8 8.196747 26.79
#> ---
#> 167: 1 2020-03-19 2020-03-21 2 -1.0 18.814871 102.84
#> 168: 1 2020-03-19 2020-03-22 3 1.0 27.170116 139.98
#> 169: 1 2020-03-20 2020-03-21 1 -1.0 16.711055 75.32
#> 170: 1 2020-03-20 2020-03-22 2 1.0 15.009446 153.53
#> 171: 1 2020-03-21 2020-03-22 1 1.0 49.980404 173.05
#> overprediction underprediction dispersion log_score mad ae_median
#> <num> <num> <num> <num> <num> <num>
#> 1: 0.2 0.0 1.72 2.810788 4.4478 3.0
#> 2: 8.4 0.0 1.63 5.814449 6.6717 14.5
#> 3: 4.6 0.0 3.93 4.242292 9.6369 16.0
#> 4: 17.4 0.0 5.03 5.729672 14.8260 32.5
#> 5: 20.8 0.0 5.99 6.082825 19.2738 33.0
#> ---
#> 167: 0.0 96.5 6.34 24.549517 20.7564 110.5
#> 168: 131.1 0.0 8.88 14.136924 40.7715 159.5
#> 169: 0.0 68.5 6.82 9.908901 27.4281 92.5
#> 170: 138.7 0.0 14.83 9.820728 58.5627 186.0
#> 171: 163.5 0.0 9.55 37.218259 24.4629 178.5
#> se_mean
#> <num>
#> 1: 17.64
#> 2: 114.49
#> 3: 59.29
#> 4: 445.21
#> 5: 767.29
#> ---
#> 167: 14665.21
#> 168: 25632.01
#> 169: 8372.25
#> 170: 37094.76
#> 171: 35494.56- All functionality outlined above can be applied to this output but a
special plotting function (
plot_forecast_evaluation) is also provided. First evaluate the Rt forecast against observed values.
plot_forecast_evaluation(model_eval$forecast_rts,
EpiSoon::example_obs_rts,
horizon_to_plot = 7
)
- Then evaluate forecast cases against observed values.
plot_forecast_evaluation(model_eval$forecast_cases,
EpiSoon::example_obs_cases,
horizon_to_plot = 7
)
Wrapper functions
EpiSoon provides several wrapper functions
(compare_models and compare_timeseries). These
both wrap evaluate_model and can be used to rapidly explore
several forecasting models (compare_models) against
multiple time series (compare_timeseries). All lower level
summary and plotting functions can be then used with the output of these
wrappers to explore the results. See the function documentation for
further details.
Supporting generic modelling packages
EpiSoon supports the use of generic forecasting models
if they are used in a wrapper that accepts a standardised set of inputs
and outputs its forecast in the form the package expects. Examples of
model wrappers are those for the bsts and
fable packages (bsts_model and
fable_model). See the examples and documentation for
fable_model for further details.