Determine the short term trend of a timeseries
Source:R/signal_detection_hlm.R
signal_detection_hlm.RdThe method is based upon a published analytics strategy by Benedetti (2019) <doi:10.5588/pha.19.0002>.
Usage
signal_detection_hlm(x, ...)
# S3 method for csfmt_rts_data_v1
signal_detection_hlm(
x,
value,
baseline_isoyears = 5,
remove_last_isoyearweeks = 0,
forecast_isoyearweeks = 2,
value_naming_prefix = "from_numerator",
remove_training_data = FALSE,
...
)Arguments
- x
Data object
- ...
Not in use.
- value
Character of name of value
- baseline_isoyears
Number of years in the past you want to include as baseline
- remove_last_isoyearweeks
Number of isoyearweeks you want to remove at the end (due to unreliable data)
- forecast_isoyearweeks
Number of isoyearweeks you want to forecast into the future
- value_naming_prefix
"from_numerator", "generic", or a custom prefix
- remove_training_data
Boolean. If TRUE, removes the training data (i.e. 1:(trend_isoyearweeks-1)) from the returned dataset.
Value
The original csfmt_rts_data_v1 dataset with extra columns. *_trend*_status contains a factor with levels c("training", "forecast", "decreasing", "null", "increasing"), while *_doublingdays* contains the expected number of days before the numerator doubles.
Examples
d <- cstidy::nor_covid19_icu_and_hospitalization_csfmt_rts_v1
d <- d[granularity_time=="isoyearweek"]
res <- csalert::signal_detection_hlm(
d,
value = "hospitalization_with_covid19_as_primary_cause_n",
baseline_isoyears = 1
)
print(res[, .(
isoyearweek,
hospitalization_with_covid19_as_primary_cause_n,
hospitalization_with_covid19_as_primary_cause_forecasted_n,
hospitalization_with_covid19_as_primary_cause_forecasted_n_forecast,
hospitalization_with_covid19_as_primary_cause_baseline_predinterval_q50x0_n,
hospitalization_with_covid19_as_primary_cause_baseline_predinterval_q99x5_n,
hospitalization_with_covid19_as_primary_cause_n_status
)])
#> isoyearweek hospitalization_with_covid19_as_primary_cause_n
#> 1: 2020-08 0
#> 2: 2020-09 0
#> 3: 2020-10 2
#> 4: 2020-11 50
#> 5: 2020-12 188
#> ---
#> 114: 2022-16 137
#> 115: 2022-17 74
#> 116: 2022-18 10
#> 117: 2022-19 NA
#> 118: 2022-20 NA
#> hospitalization_with_covid19_as_primary_cause_forecasted_n
#> 1: 0
#> 2: 0
#> 3: 2
#> 4: 50
#> 5: 188
#> ---
#> 114: 137
#> 115: 74
#> 116: 10
#> 117: 66
#> 118: 59
#> hospitalization_with_covid19_as_primary_cause_forecasted_n_forecast
#> 1: FALSE
#> 2: FALSE
#> 3: FALSE
#> 4: FALSE
#> 5: FALSE
#> ---
#> 114: FALSE
#> 115: FALSE
#> 116: FALSE
#> 117: TRUE
#> 118: TRUE
#> hospitalization_with_covid19_as_primary_cause_baseline_predinterval_q50x0_n
#> 1: NA
#> 2: NA
#> 3: NA
#> 4: NA
#> 5: NA
#> ---
#> 114: 125
#> 115: 92
#> 116: 69
#> 117: 66
#> 118: 59
#> hospitalization_with_covid19_as_primary_cause_baseline_predinterval_q99x5_n
#> 1: NA
#> 2: NA
#> 3: NA
#> 4: NA
#> 5: NA
#> ---
#> 114: 255
#> 115: 184
#> 116: 77
#> 117: 79
#> 118: 79
#> hospitalization_with_covid19_as_primary_cause_n_status
#> 1: training
#> 2: training
#> 3: training
#> 4: training
#> 5: training
#> ---
#> 114: null
#> 115: null
#> 116: null
#> 117: forecast
#> 118: forecast