short_term_trend {csalert}

Determine the short term trend of a timeseries

Description

The method is based upon a published analytics strategy by Benedetti (2019) <doi:10.5588/pha.19.0002>.

Usage

short_term_trend(x, ...)

## S3 method for class 'csfmt_rts_data_v1'
short_term_trend(
  x,
  numerator,
  denominator = NULL,
  prX = 100,
  trend_isoyearweeks = 6,
  remove_last_isoyearweeks = 0,
  forecast_isoyearweeks = trend_isoyearweeks,
  numerator_naming_prefix = "from_numerator",
  denominator_naming_prefix = "from_denominator",
  statistics_naming_prefix = "universal",
  remove_training_data = FALSE,
  include_decreasing = FALSE,
  alpha = 0.05,
  ...
)

Arguments

x

Data object

...

Not in use.

numerator

Character of name of numerator

denominator

Character of name of denominator (optional)

prX

If using denominator, what scaling factor should be used for numerator/denominator?

trend_isoyearweeks

Same as trend_dates, but used if granularity_geo=='isoyearweek'

remove_last_isoyearweeks

Same as remove_last_dates, but used if granularity_geo=='isoyearweek'

forecast_isoyearweeks

Same as forecast_dates, but used if granularity_geo=='isoyearweek'

numerator_naming_prefix

"from_numerator", "generic", or a custom prefix

denominator_naming_prefix

"from_denominator", "generic", or a custom prefix

statistics_naming_prefix

"universal" (one variable for trend status, one variable for doubling dates), "from_numerator_and_prX" (If denominator is NULL, then one variable corresponding to numerator. If denominator exists, then one variable for each of the prXs)

remove_training_data

Boolean. If TRUE, removes the training data (i.e. 1:(trend_dates-1) or 1:(trend_isoyearweeks-1)) from the returned dataset.

include_decreasing

If true, then *_trend*_status contains the levels c("training", "forecast", "decreasing", "null", "increasing"), otherwise the levels c("training", "forecast", "notincreasing", "increasing").

alpha

Significance level for change in trend.

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::short_term_trend(
  d,
  numerator = "hospitalization_with_covid19_as_primary_cause_n",
  trend_isoyearweeks = 6
)
print(res[, .(
  isoyearweek,
  hospitalization_with_covid19_as_primary_cause_n,
  hospitalization_with_covid19_as_primary_cause_trend0_41_status
)])