Running ‘testthat.R’ [9m/11m]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(finnts)
  Loading required package: modeltime
  > 
  > test_check("finnts")
  
  ── Prepping Data ───────────────────────────────────────────────────────────────
  Joining with `by = join_by(Combo, Date)`
  
  ── Kicking off Finn Modeling Process ───────────────────────────────────────────
  
  ── Running Combo: M750 ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Creating Simple Model Averages ──────────────────────────────────────────────
  
  ── Final Finn Outputs ──────────────────────────────────────────────────────────
  
  ── Selecting Best Model 
  Joining with `by = join_by(Combo, Model)`
  Joining with `by = join_by(Combo, Model)`
  Joining with `by = join_by(Combo, Model)`
  Joining with `by = join_by(Combo, Model)`
   [1] "First Dt Val M750"       "First Dt Val M750"      
   [3] "First Dt Val 16587"      "First Dt Val Historical"
   [5] "First Dt Val NA"         "First Dt Val 11000"     
   [7] "First Dt Val 11000"      "First Dt Val 11000"     
   [9] "First Dt Val 11000"      "First Dt Val 11000"     
  
  ── Prepping Data ───────────────────────────────────────────────────────────────
  Since argument characters are not specified, the default labelling system is used.
  Joining with `by = join_by(Combo, Date)`
  
  ── Kicking off Finn Modeling Process ───────────────────────────────────────────
  
  ── Running Combo: A ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: A..AA ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: A..AB ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: A..AC ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: B ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: B..BA ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: B..BB ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: Total ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Creating Simple Model Averages ──────────────────────────────────────────────
  
  ── Final Finn Outputs ──────────────────────────────────────────────────────────
  
  ── Selecting Best Model 
  
  ── Reconciling Hierarchical Forecast 
  Joining with `by = join_by(Combo, Model)`
  Joining with `by = join_by(Combo, Model)`
  Since argument characters are not specified, the default labelling system is used.
  Joining with `by = join_by(Date, Combo)`
  Joining with `by = join_by(Combo, Model)`
   [1] "First Dt Val B--BB"           "First Dt Val B"              
   [3] "First Dt Val BB"              "First Dt Val 11504"          
   [5] "First Dt Val Historical"      "First Dt Val NA"             
   [7] "First Dt Val 12.537166248181" "First Dt Val 12.537166248181"
   [9] "First Dt Val 12.537166248181" "First Dt Val 12.537166248181"
  [11] "First Dt Val 12.537166248181"
  
  ── Prepping Data ───────────────────────────────────────────────────────────────
  Joining with `by = join_by(Combo, Date)`
  
  ── Kicking off Finn Modeling Process ───────────────────────────────────────────
  
  ── Running Combo: ACT..female ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: ACT..male ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: NSW..female ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: NSW..male ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: NT..female ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: NT..male ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: QLD..female ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: QLD..male ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: SA..female ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: SA..male ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: Sex.female ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: Sex.male ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: State.ACT ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: State.NSW ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: State.NT ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: State.QLD ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: State.SA ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: State.TAS ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: State.VIC ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: State.WA ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: TAS..female ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: TAS..male ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: Total ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: VIC..female ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: VIC..male ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: WA..female ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Running Combo: WA..male ──
  
  ── Initial Feature Engineering 
  
  ── Individual Model Training 
  ✔ Known model:meanf function:meanf
  ✔ Known model:snaive function:snaive
  ℹ Function being called: meanf
  ✔ meanf
  ℹ Function being called: snaive
  ✔ snaive
  # Modeltime Table
  # A tibble: 2 × 3
    .model_id .model     .model_desc
        <int> <list>     <chr>      
  1         1 <workflow> snaive     
  2         2 <workflow> meanf      
  # Modeltime Table
  # A tibble: 0 × 3
  # … with 3 variables: .model_id <int>, .model <list>, .model_desc <chr>
  
  ── Refitting Individual Models 
  Data is not ordered by the 'date_var'. Resamples will be arranged by `Date`.
  Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
  Joining with `by = join_by(.id, .row)`
  
  ── Forecast Output 
  
  ── Creating Simple Model Averages ──────────────────────────────────────────────
  
  ── Final Finn Outputs ──────────────────────────────────────────────────────────
  
  ── Selecting Best Model 
  
  ── Reconciling Hierarchical Forecast 
  Joining with `by = join_by(Combo, Model)`
  Joining with `by = join_by(Combo, Model)`
  Joining with `by = join_by(Date, Combo)`
  Joining with `by = join_by(Combo, Model)`
   [1] "First Dt Val WA--male"   "First Dt Val WA"        
   [3] "First Dt Val male"       "First Dt Val 12234"     
   [5] "First Dt Val Historical" "First Dt Val NA"        
   [7] "First Dt Val 54"         "First Dt Val 54"        
   [9] "First Dt Val 54"         "First Dt Val 54"        
  [11] "First Dt Val 54"        
  [ FAIL 2 | WARN 10 | SKIP 0 | PASS 111 ]
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Failure ('test-forecast_time_series.R:195'): final forecast data rows are meaningful ──
  nrow(future_frame) (`actual`) not equal to length(unique(inp_data_combos$Combo)) * forecast_horizon (`expected`).
  
    `actual`:  8
  `expected`: 15
  ── Failure ('test-forecast_time_series.R:294'): final forecast data rows are meaningful ──
  nrow(future_frame) (`actual`) not equal to length(unique(inp_data_combos$Combo)) * forecast_horizon (`expected`).
  
    `actual`: 27
  `expected`: 48
  
  [ FAIL 2 | WARN 10 | SKIP 0 | PASS 111 ]
  Error: Test failures
  Execution halted