# iFlex Workflow with Shipped Example Data

#### elcf4R package

# Overview

This vignette shows a complete lightweight workflow using only datasets that ship with the package:

- `elcf4r_iflex_example` for preprocessing and model fitting examples.
- `elcf4r_iflex_benchmark_results` for inspecting saved benchmark results.

# Inspect the shipped iFlex example

    dim(elcf4r_iflex_example)
    #> [1] 1008   16
    length(unique(elcf4r_iflex_example$entity_id))
    #> [1] 3
    range(elcf4r_iflex_example$timestamp)
    #> [1] "2020-01-06 00:00:00 UTC" "2020-01-19 23:00:00 UTC"
    head(elcf4r_iflex_example[, c("entity_id", "timestamp", "y", "temp")])
    #>   entity_id           timestamp     y temp
    #> 1     Exp_1 2020-01-06 00:00:00 2.043  7.6
    #> 2     Exp_1 2020-01-06 01:00:00 2.358  7.5
    #> 3     Exp_1 2020-01-06 02:00:00 2.496  7.5
    #> 4     Exp_1 2020-01-06 03:00:00 2.076  7.5
    #> 5     Exp_1 2020-01-06 04:00:00 2.065  7.5
    #> 6     Exp_1 2020-01-06 05:00:00 2.059  7.5

# Build daily segments

We work on one participant to keep the example compact.

    id1 <- subset(
      elcf4r_iflex_example,
      entity_id == unique(elcf4r_iflex_example$entity_id)[1]
    )

    daily <- elcf4r_build_daily_segments(
      id1,
      carry_cols = c("participation_phase", "price_signal")
    )

    dim(daily$segments)
    #> [1] 14 24
    head(daily$covariates[, c("date", "dow", "temp_mean", "participation_phase")])
    #>                         date       dow temp_mean participation_phase
    #> Exp_1__2020-01-06 2020-01-06    Monday  7.508333             Phase_1
    #> Exp_1__2020-01-07 2020-01-07   Tuesday  7.254167             Phase_1
    #> Exp_1__2020-01-08 2020-01-08 Wednesday  6.312500             Phase_1
    #> Exp_1__2020-01-09 2020-01-09  Thursday  3.420833             Phase_1
    #> Exp_1__2020-01-10 2020-01-10    Friday  1.445833             Phase_1
    #> Exp_1__2020-01-11 2020-01-11  Saturday  6.754167             Phase_1

# Fit forecasting models on the example panel

We train on the first 10 days and predict the 11th day.

    train_days <- daily$covariates$date[1:10]
    test_day <- daily$covariates$date[11]

    train_long <- subset(id1, date %in% train_days)
    test_long <- subset(id1, date == test_day)

    fit_gam <- elcf4r_fit_gam(
      train_long[, c("y", "time_index", "dow", "month", "temp")],
      use_temperature = TRUE
    )
    pred_gam <- predict(
      fit_gam,
      newdata = test_long[, c("y", "time_index", "dow", "month", "temp")]
    )

    fit_mars <- elcf4r_fit_mars(
      train_long[, c("y", "time_index", "dow", "month", "temp")],
      use_temperature = TRUE
    )
    pred_mars <- predict(
      fit_mars,
      newdata = test_long[, c("y", "time_index", "dow", "month", "temp")]
    )

    fit_kwf <- elcf4r_fit_kwf(
      segments = daily$segments[1:10, ],
      covariates = daily$covariates[1:10, , drop = FALSE],
      target_covariates = daily$covariates[11, , drop = FALSE]
    )
    pred_kwf <- predict(fit_kwf)

    fit_kwf_clustered <- elcf4r_fit_kwf_clustered(
      segments = daily$segments[1:10, ],
      covariates = daily$covariates[1:10, , drop = FALSE],
      target_covariates = daily$covariates[11, , drop = FALSE]
    )
    pred_kwf_clustered <- predict(fit_kwf_clustered)

    naive_day <- as.numeric(daily$segments[10, ])

    rbind(
      gam = unlist(elcf4r_metrics(test_long$y, pred_gam, naive_pred = naive_day)),
      mars = unlist(elcf4r_metrics(test_long$y, pred_mars, naive_pred = naive_day)),
      kwf = unlist(elcf4r_metrics(test_long$y, pred_kwf, naive_pred = naive_day)),
      kwf_clustered = unlist(elcf4r_metrics(test_long$y, pred_kwf_clustered, naive_pred = naive_day))
    )
    #>                    nmae     nrmse     smape      mase
    #> gam           0.1961463 0.2355394 0.1796715 0.9274129
    #> mars          0.1690549 0.1920249 0.1600477 0.7993201
    #> kwf           0.1404839 0.1821267 0.1394477 0.6642319
    #> kwf_clustered 0.1640983 0.1907997 0.1529166 0.7758848

An LSTM example is available when the `keras3` and `tensorflow` packages are installed and Python has been selected explicitly for `reticulate`, for example with `elcf4r_use_tensorflow_env()` or `reticulate::use_virtualenv()` before rendering this vignette:

    fit_lstm <- elcf4r_fit_lstm(
      segments = daily$segments[1:10, ],
      covariates = daily$covariates[1:10, , drop = FALSE],
      use_temperature = TRUE,
      epochs = 1,
      units = 4,
      batch_size = 2,
      verbose = 0
    )

    pred_lstm <- predict(fit_lstm)
    unlist(elcf4r_metrics(test_long$y, pred_lstm, naive_pred = naive_day))
    #>      nmae     nrmse     smape      mase
    #> 0.2269092 0.2579581 0.2107226 1.0728651

    "LSTM example skipped because no explicit Keras/TensorFlow backend is configured in this R environment."

# Run a small rolling benchmark

The package also exposes a reusable rolling-origin benchmark runner that works on normalized panels produced by the `elcf4r_read_*()` adapters.

    benchmark_index <- elcf4r_build_benchmark_index(
      elcf4r_iflex_example,
      carry_cols = c("dataset", "participation_phase", "price_signal")
    )

    benchmark_small <- elcf4r_benchmark(
      panel = elcf4r_iflex_example,
      benchmark_index = benchmark_index,
      methods = c("gam", "kwf"),
      cohort_size = 1,
      train_days = 10,
      test_days = 2,
      include_predictions = FALSE
    )

    benchmark_small$results
    #>                                 benchmark_name dataset entity_id method
    #> 1 iflex_hourly_1_ids_10_train_2_test_2_methods   iflex     Exp_1    gam
    #> 2 iflex_hourly_1_ids_10_train_2_test_2_methods   iflex     Exp_1    kwf
    #> 3 iflex_hourly_1_ids_10_train_2_test_2_methods   iflex     Exp_1    gam
    #> 4 iflex_hourly_1_ids_10_train_2_test_2_methods   iflex     Exp_1    kwf
    #>    test_date train_start  train_end train_days test_points use_temperature
    #> 1 2020-01-16  2020-01-06 2020-01-15         10          24            TRUE
    #> 2 2020-01-16  2020-01-06 2020-01-15         10          24            TRUE
    #> 3 2020-01-17  2020-01-07 2020-01-16         10          24            TRUE
    #> 4 2020-01-17  2020-01-07 2020-01-16         10          24            TRUE
    #>   thermosensitive     thermosensitivity_status thermosensitivity_ratio
    #> 1              NA insufficient_summer_coverage                      NA
    #> 2              NA insufficient_summer_coverage                      NA
    #> 3              NA insufficient_summer_coverage                      NA
    #> 4              NA insufficient_summer_coverage                      NA
    #>   fit_seconds status error_message      nmae     nrmse     smape      mase
    #> 1       0.017     ok          <NA> 0.1961463 0.2355394 0.1796715 0.9274129
    #> 2       0.011     ok          <NA> 0.1404839 0.1821267 0.1394477 0.6642319
    #> 3       0.017     ok          <NA> 0.3692809 0.4462295 0.1641611 1.3876600
    #> 4       0.011     ok          <NA> 0.5393512 0.6402973 0.2242763 2.0267394

# Inspect shipped benchmark results

The package also ships precomputed benchmark results on a fixed iFlex cohort. In the current build these results cover `gam`, `mars`, `kwf`, `kwf_clustered` and `lstm`.

    head(elcf4r_iflex_benchmark_results)
    #>                                  benchmark_name dataset entity_id        method
    #> 1 iflex_hourly_15_ids_28_train_7_test_5_methods   iflex     Exp_1           gam
    #> 2 iflex_hourly_15_ids_28_train_7_test_5_methods   iflex     Exp_1          mars
    #> 3 iflex_hourly_15_ids_28_train_7_test_5_methods   iflex     Exp_1           kwf
    #> 4 iflex_hourly_15_ids_28_train_7_test_5_methods   iflex     Exp_1 kwf_clustered
    #> 5 iflex_hourly_15_ids_28_train_7_test_5_methods   iflex     Exp_1          lstm
    #> 6 iflex_hourly_15_ids_28_train_7_test_5_methods   iflex     Exp_1           gam
    #>    test_date train_start  train_end train_days test_points use_temperature
    #> 1 2020-02-03  2020-01-06 2020-02-02         28          24            TRUE
    #> 2 2020-02-03  2020-01-06 2020-02-02         28          24            TRUE
    #> 3 2020-02-03  2020-01-06 2020-02-02         28          24            TRUE
    #> 4 2020-02-03  2020-01-06 2020-02-02         28          24            TRUE
    #> 5 2020-02-03  2020-01-06 2020-02-02         28          24            TRUE
    #> 6 2020-02-04  2020-01-07 2020-02-03         28          24            TRUE
    #>   thermosensitive     thermosensitivity_status thermosensitivity_ratio
    #> 1              NA insufficient_summer_coverage                      NA
    #> 2              NA insufficient_summer_coverage                      NA
    #> 3              NA insufficient_summer_coverage                      NA
    #> 4              NA insufficient_summer_coverage                      NA
    #> 5              NA insufficient_summer_coverage                      NA
    #> 6              NA insufficient_summer_coverage                      NA
    #>   fit_seconds status error_message      nmae     nrmse      smape      mase
    #> 1       0.073     ok          <NA> 0.2241390 0.3029546 0.11050062 0.7570273
    #> 2       0.062     ok          <NA> 0.2986949 0.3565707 0.15316209 1.0088391
    #> 3       0.067     ok          <NA> 0.2622522 0.3346779 0.13014701 0.8857544
    #> 4       0.184     ok          <NA> 0.2039969 0.2643067 0.09952173 0.6889975
    #> 5       1.229     ok          <NA> 0.3100784 0.3850647 0.15454029 1.0472868
    #> 6       0.024     ok          <NA> 0.1502679 0.2054841 0.12878440 0.8595982

    aggregate(
      cbind(nmae, nrmse, smape, mase, fit_seconds) ~ method,
      data = elcf4r_iflex_benchmark_results,
      FUN = function(x) round(mean(x, na.rm = TRUE), 4)
    )
    #>          method   nmae  nrmse  smape   mase fit_seconds
    #> 1           gam 0.2275 0.2908 0.3116 0.8519      0.0238
    #> 2           kwf 0.2469 0.3125 0.3303 0.9083      0.0344
    #> 3 kwf_clustered 0.2561 0.3289 0.3351 0.9335      0.1276
    #> 4          lstm 0.2365 0.2967 0.3373 0.9129      1.2425
    #> 5          mars 0.2209 0.2808 0.3031 0.8220      0.0132

This object is intended to support reproducible package examples and to provide a stable reference point for future benchmark extensions.