kantime

{kantime} is a minimal wrapper for Time Series Kolmogorov Arnold Networks in R. By binding nixtla’s {neuralforecast} engine, KAN, from Python to use in R. Additional steps were made to bridged to {modeltime}, hence the name {kantime}!

Installation

You can install the development version of kantime like so:

devtools::install_github("frankiethull/kantime")

Example

This is a basic example showing the barebones {reticulate} Python wrappers. These are the core bindings before registering can configuring to {parsnip} and then bridging to {modeltime}.

setup {kantime}

kantime requires python and neuralforecast.

# load the R library -------------------
library(kantime)

# setup Python environment -------------
# 1) create a virtual env   ~ 
# kantime::create_neuralforecast_env()

# 2) use the virtual env    ~
kantime::use_neuralforecast_env()

# 3) install neuralforecast ~  
#kantime::install_neuralforecast()

example data for testing the library:

## data comes from sister repo

air_passengers_df <- readr::read_csv("https://raw.githubusercontent.com/frankiethull/nixtla-r-tutorial/refs/heads/main/airpassengersDF.csv", show_col_types = FALSE)

Sys.sleep(1)

train_df <- air_passengers_df |> dplyr::slice(1:132)
test_df  <- air_passengers_df |> dplyr::anti_join(train_df)
#> Joining with `by = join_by(unique_id, ds, y)`

barebone internals, no xregs, with conformal prediction

note that nixtla’s design requires a unique_id for each unique time series, ds for the time column, and y for the outcome variable. The internals work as minimal python wrappers to fit and predict on a time series data like so:

# kan specs --
kan_model_specs <- kantime:::kan_spec(h = 12L)

# fit
kan_model_fit   <- kantime:::conformal_fit(model_spec = kan_model_specs, df = train_df)

# predict
conf_kan_preds  <- kantime:::conformal_predict(
                                     model_spec = kan_model_specs, 
                                     model_fit = kan_model_fit,
                                     level = 90L)

These internals are built thanks to {reticulate} and of using Nixtla’s {neuralforecast} via R. While the idea works ok, the results and workflow can be clunky for R, especially when building for many unique IDs (many models for different time series loses pandas row IDs). Additionally, handling parallel processes in R via {reticulate} for cv, tuning and training in Python requires additional robustness.

{kantime} is super experimental in it’s design. Which is also why this is not a full {neuralforecast} binding. This binding in particular is going to leverage the root KAN model, a few helper utils, then bind the base KAN model to {parsnip} and bridge to {modeltime}. This loses pieces of the underlying Nixtla functionality but replacing with {tidymodels} & {modeltime} functionality.

{neuralforecast}’s kan with a {modeltime} bridge

given our underlying internals, we can bind these functions to {modeltime} which is similar to registering a parsnip model but requires an additional bridge. The bridge implementation is shown below, but we have to add predict methods and there’s quite a bit of underlying work that makes it fully functioning.

kan_bridge <- kantime:::kan_bridge_fit_impl(
  x = train_df |> dplyr::select(-y),
  y = train_df |> dplyr::pull(y),
  h = 12L,
  input_size = 24L,
  max_steps = 10L,
  freq = "ME"
)

kan_bridge |> predict()

{kantime} workflow within {modeltime}

Remembering that once the model has been bridged to {modeltime}, we lose some of the underlying nixtla utilities, but get access to tidymodels + modeltime utilities in R. What’s this mean? We can now use this KAN binding with tidymodels tools like initial_time_split and we can use leverage modeltime’s toolkit to further calibrate & validate KAN models. In fact, we went through these additional bridging steps to have a full suite of backtesting, ensembling, calibrating, and scoring tools that will be very familiar if already using {modeltime} and/or {tidymodels}.

library(parsnip)
library(modeltime)

# time split
splits   <- rsample::initial_time_split(air_passengers_df, prop = .92)
training <- rsample::training(splits)
testing  <- rsample::testing(splits)

# kantime fit
kantime_fit <- kan(h = 12L,
                   input_size = 24L,
                   max_steps = 10L,
                   freq = "ME") |>
                set_engine("kan") |>
               fit(y ~ ds + unique_id, data = training)
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train_loss_epoch=1.790]        Epoch 2:   0%|          | 0/1 [00:00<?, ?it/s, v_num=102, train_loss_step=1.790, train_loss_epoch=1.790]Epoch 2: 100%|██████████| 1/1 [00:00<00:00,  1.20it/s, v_num=102, train_loss_step=1.790, train_loss_epoch=1.790]Epoch 2: 100%|██████████| 1/1 [00:00<00:00,  1.20it/s, v_num=102, train_loss_step=1.730, train_loss_epoch=1.790]Epoch 2: 100%|██████████| 1/1 [00:00<00:00,  1.19it/s, v_num=102, train_loss_step=1.730, train_loss_epoch=1.730]Epoch 2:   0%|          | 0/1 [00:00<?, ?it/s, v_num=102, train_loss_step=1.730, train_loss_epoch=1.730]        Epoch 3:   0%|          | 0/1 [00:00<?, ?it/s, v_num=102, train_loss_step=1.730, train_loss_epoch=1.730]Epoch 3: 100%|██████████| 1/1 [00:00<00:00,  1.21it/s, v_num=102, train_loss_step=1.730, train_loss_epoch=1.730]Epoch 3: 100%|██████████| 1/1 [00:00<00:00,  1.21it/s, v_num=102, train_loss_step=1.680, train_loss_epoch=1.730]Epoch 3: 100%|██████████| 1/1 [00:00<00:00,  1.21it/s, v_num=102, train_loss_step=1.680, train_loss_epoch=1.680]Epoch 3:   0%|          | 0/1 [00:00<?, ?it/s, v_num=102, train_loss_step=1.680, train_loss_epoch=1.680]        Epoch 4:   0%|          | 0/1 [00:00<?, ?it/s, v_num=102, train_loss_step=1.680, train_loss_epoch=1.680]Epoch 4: 100%|██████████| 1/1 [00:00<00:00,  1.20it/s, v_num=102, train_loss_step=1.680, train_loss_epoch=1.680]Epoch 4: 100%|██████████| 1/1 [00:00<00:00,  1.20it/s, v_num=102, train_loss_step=1.640, train_loss_epoch=1.680]Epoch 4: 100%|██████████| 1/1 [00:00<00:00,  1.20it/s, v_num=102, train_loss_step=1.640, train_loss_epoch=1.640]Epoch 4:   0%|          | 0/1 [00:00<?, ?it/s, v_num=102, train_loss_step=1.640, train_loss_epoch=1.640]        Epoch 5:   0%|          | 0/1 [00:00<?, ?it/s, v_num=102, train_loss_step=1.640, train_loss_epoch=1.640]Epoch 5: 100%|██████████| 1/1 [00:00<00:00,  1.21it/s, v_num=102, train_loss_step=1.640, train_loss_epoch=1.640]Epoch 5: 100%|██████████| 1/1 [00:00<00:00,  1.21it/s, v_num=102, train_loss_step=1.560, train_loss_epoch=1.640]Epoch 5: 100%|██████████| 1/1 [00:00<00:00,  1.21it/s, v_num=102, train_loss_step=1.560, train_loss_epoch=1.560]Epoch 5:   0%|          | 0/1 [00:00<?, ?it/s, v_num=102, train_loss_step=1.560, train_loss_epoch=1.560]        Epoch 6:   0%|          | 0/1 [00:00<?, ?it/s, v_num=102, train_loss_step=1.560, train_loss_epoch=1.560]Epoch 6: 100%|██████████| 1/1 [00:00<00:00,  1.15it/s, v_num=102, train_loss_step=1.560, train_loss_epoch=1.560]Epoch 6: 100%|██████████| 1/1 [00:00<00:00,  1.13it/s, v_num=102, train_loss_step=1.490, train_loss_epoch=1.560]Epoch 6: 100%|██████████| 1/1 [00:00<00:00,  1.13it/s, v_num=102, train_loss_step=1.490, train_loss_epoch=1.490]Epoch 6:   0%|          | 0/1 [00:00<?, ?it/s, v_num=102, train_loss_step=1.490, train_loss_epoch=1.490]        Epoch 7:   0%|          | 0/1 [00:00<?, ?it/s, v_num=102, train_loss_step=1.490, train_loss_epoch=1.490]Epoch 7: 100%|██████████| 1/1 [00:00<00:00,  1.18it/s, v_num=102, train_loss_step=1.490, train_loss_epoch=1.490]Epoch 7: 100%|██████████| 1/1 [00:00<00:00,  1.18it/s, v_num=102, train_loss_step=1.450, train_loss_epoch=1.490]Epoch 7: 100%|██████████| 1/1 [00:00<00:00,  1.18it/s, v_num=102, train_loss_step=1.450, train_loss_epoch=1.450]Epoch 7:   0%|          | 0/1 [00:00<?, ?it/s, v_num=102, train_loss_step=1.450, train_loss_epoch=1.450]        Epoch 8:   0%|          | 0/1 [00:00<?, ?it/s, v_num=102, train_loss_step=1.450, train_loss_epoch=1.450]Epoch 8: 100%|██████████| 1/1 [00:00<00:00,  1.17it/s, v_num=102, train_loss_step=1.450, train_loss_epoch=1.450]Epoch 8: 100%|██████████| 1/1 [00:00<00:00,  1.17it/s, v_num=102, train_loss_step=1.400, train_loss_epoch=1.450]Epoch 8: 100%|██████████| 1/1 [00:00<00:00,  1.17it/s, v_num=102, train_loss_step=1.400, train_loss_epoch=1.400]Epoch 8:   0%|          | 0/1 [00:00<?, ?it/s, v_num=102, train_loss_step=1.400, train_loss_epoch=1.400]        Epoch 9:   0%|          | 0/1 [00:00<?, ?it/s, v_num=102, train_loss_step=1.400, train_loss_epoch=1.400]Epoch 9: 100%|██████████| 1/1 [00:00<00:00,  1.20it/s, v_num=102, train_loss_step=1.400, train_loss_epoch=1.400]Epoch 9: 100%|██████████| 1/1 [00:00<00:00,  1.20it/s, v_num=102, train_loss_step=1.310, train_loss_epoch=1.400]
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#>                                                               �[AEpoch 9: 100%|██████████| 1/1 [00:00<00:00,  1.20it/s, v_num=102, train_loss_step=1.310, train_loss_epoch=1.400]Epoch 9: 100%|██████████| 1/1 [00:00<00:00,  1.20it/s, v_num=102, train_loss_step=1.310, train_loss_epoch=1.310]Epoch 9: 100%|██████████| 1/1 [00:00<00:00,  1.18it/s, v_num=102, train_loss_step=1.310, train_loss_epoch=1.310]
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[00:00<?, ?it/s]Training:   0%|          | 0/1 [00:00<?, ?it/s]Epoch 0:   0%|          | 0/1 [00:00<?, ?it/s] Epoch 0: 100%|██████████| 1/1 [00:00<00:00,  1.03it/s]Epoch 0: 100%|██████████| 1/1 [00:00<00:00,  1.03it/s, v_num=104, train_loss_step=1.210]Epoch 0: 100%|██████████| 1/1 [00:00<00:00,  1.03it/s, v_num=104, train_loss_step=1.210, train_loss_epoch=1.210]Epoch 0:   0%|          | 0/1 [00:00<?, ?it/s, v_num=104, train_loss_step=1.210, train_loss_epoch=1.210]        Epoch 1:   0%|          | 0/1 [00:00<?, ?it/s, v_num=104, train_loss_step=1.210, train_loss_epoch=1.210]Epoch 1: 100%|██████████| 1/1 [00:00<00:00,  1.20it/s, v_num=104, train_loss_step=1.210, train_loss_epoch=1.210]Epoch 1: 100%|██████████| 1/1 [00:00<00:00,  1.20it/s, v_num=104, train_loss_step=1.150, train_loss_epoch=1.210]Epoch 1: 100%|██████████| 1/1 [00:00<00:00,  1.20it/s, v_num=104, train_loss_step=1.150, train_loss_epoch=1.150]Epoch 1:   0%|          | 0/1 [00:00<?, ?it/s, v_num=104, train_loss_step=1.150, train_loss_epoch=1.150]        Epoch 2:   0%|          | 0/1 [00:00<?, ?it/s, v_num=104, train_loss_step=1.150, train_loss_epoch=1.150]Epoch 2: 100%|██████████| 1/1 [00:00<00:00,  1.19it/s, v_num=104, train_loss_step=1.150, train_loss_epoch=1.150]Epoch 2: 100%|██████████| 1/1 [00:00<00:00,  1.19it/s, v_num=104, train_loss_step=1.080, train_loss_epoch=1.150]Epoch 2: 100%|██████████| 1/1 [00:00<00:00,  1.19it/s, v_num=104, train_loss_step=1.080, train_loss_epoch=1.080]Epoch 2:   0%|          | 0/1 [00:00<?, ?it/s, v_num=104, train_loss_step=1.080, train_loss_epoch=1.080]        Epoch 3:   0%|          | 0/1 [00:00<?, ?it/s, v_num=104, train_loss_step=1.080, train_loss_epoch=1.080]Epoch 3: 100%|██████████| 1/1 [00:00<00:00,  1.16it/s, v_num=104, train_loss_step=1.080, train_loss_epoch=1.080]Epoch 3: 100%|██████████| 1/1 [00:00<00:00,  1.16it/s, v_num=104, train_loss_step=1.020, train_loss_epoch=1.080]Epoch 3: 100%|██████████| 1/1 [00:00<00:00,  1.16it/s, v_num=104, train_loss_step=1.020, train_loss_epoch=1.020]Epoch 3:   0%|          | 0/1 [00:00<?, ?it/s, v_num=104, train_loss_step=1.020, train_loss_epoch=1.020]        Epoch 4:   0%|          | 0/1 [00:00<?, ?it/s, v_num=104, train_loss_step=1.020, train_loss_epoch=1.020]Epoch 4: 100%|██████████| 1/1 [00:00<00:00,  1.12it/s, v_num=104, train_loss_step=1.020, train_loss_epoch=1.020]Epoch 4: 100%|██████████| 1/1 [00:00<00:00,  1.11it/s, v_num=104, train_loss_step=0.979, train_loss_epoch=1.020]Epoch 4: 100%|██████████| 1/1 [00:00<00:00,  1.11it/s, v_num=104, train_loss_step=0.979, train_loss_epoch=0.979]Epoch 4:   0%|          | 0/1 [00:00<?, ?it/s, v_num=104, train_loss_step=0.979, train_loss_epoch=0.979]        Epoch 5:   0%|          | 0/1 [00:00<?, ?it/s, v_num=104, train_loss_step=0.979, train_loss_epoch=0.979]Epoch 5: 100%|██████████| 1/1 [00:00<00:00,  1.19it/s, v_num=104, train_loss_step=0.979, train_loss_epoch=0.979]Epoch 5: 100%|██████████| 1/1 [00:00<00:00,  1.19it/s, v_num=104, train_loss_step=0.909, train_loss_epoch=0.979]Epoch 5: 100%|██████████| 1/1 [00:00<00:00,  1.19it/s, v_num=104, train_loss_step=0.909, train_loss_epoch=0.909]Epoch 5:   0%|          | 0/1 [00:00<?, ?it/s, v_num=104, train_loss_step=0.909, train_loss_epoch=0.909]        Epoch 6:   0%|          | 0/1 [00:00<?, ?it/s, v_num=104, train_loss_step=0.909, train_loss_epoch=0.909]Epoch 6: 100%|██████████| 1/1 [00:00<00:00,  1.21it/s, v_num=104, train_loss_step=0.909, train_loss_epoch=0.909]Epoch 6: 100%|██████████| 1/1 [00:00<00:00,  1.21it/s, v_num=104, train_loss_step=0.859, train_loss_epoch=0.909]Epoch 6: 100%|██████████| 1/1 [00:00<00:00,  1.21it/s, v_num=104, train_loss_step=0.859, train_loss_epoch=0.859]Epoch 6:   0%|          | 0/1 [00:00<?, ?it/s, v_num=104, train_loss_step=0.859, train_loss_epoch=0.859]        Epoch 7:   0%|          | 0/1 [00:00<?, ?it/s, v_num=104, train_loss_step=0.859, train_loss_epoch=0.859]Epoch 7: 100%|██████████| 1/1 [00:00<00:00,  1.20it/s, v_num=104, train_loss_step=0.859, train_loss_epoch=0.859]Epoch 7: 100%|██████████| 1/1 [00:00<00:00,  1.20it/s, v_num=104, train_loss_step=0.803, train_loss_epoch=0.859]Epoch 7: 100%|██████████| 1/1 [00:00<00:00,  1.20it/s, v_num=104, train_loss_step=0.803, train_loss_epoch=0.803]Epoch 7:   0%|          | 0/1 [00:00<?, ?it/s, v_num=104, train_loss_step=0.803, train_loss_epoch=0.803]        Epoch 8:   0%|          | 0/1 [00:00<?, ?it/s, v_num=104, train_loss_step=0.803, train_loss_epoch=0.803]Epoch 8: 100%|██████████| 1/1 [00:00<00:00,  1.12it/s, v_num=104, train_loss_step=0.803, train_loss_epoch=0.803]Epoch 8: 100%|██████████| 1/1 [00:00<00:00,  1.12it/s, v_num=104, train_loss_step=0.767, train_loss_epoch=0.803]Epoch 8: 100%|██████████| 1/1 [00:00<00:00,  1.12it/s, v_num=104, train_loss_step=0.767, train_loss_epoch=0.767]Epoch 8:   0%|          | 0/1 [00:00<?, ?it/s, v_num=104, train_loss_step=0.767, train_loss_epoch=0.767]        Epoch 9:   0%|          | 0/1 [00:00<?, ?it/s, v_num=104, train_loss_step=0.767, train_loss_epoch=0.767]Epoch 9: 100%|██████████| 1/1 [00:00<00:00,  1.19it/s, v_num=104, train_loss_step=0.767, train_loss_epoch=0.767]Epoch 9: 100%|██████████| 1/1 [00:00<00:00,  1.19it/s, v_num=104, train_loss_step=0.731, train_loss_epoch=0.767]
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#>                                                               �[AEpoch 9: 100%|██████████| 1/1 [00:00<00:00,  1.18it/s, v_num=104, train_loss_step=0.731, train_loss_epoch=0.767]Epoch 9: 100%|██████████| 1/1 [00:00<00:00,  1.18it/s, v_num=104, train_loss_step=0.731, train_loss_epoch=0.731]Epoch 9: 100%|██████████| 1/1 [00:00<00:00,  1.18it/s, v_num=104, train_loss_step=0.731, train_loss_epoch=0.731]

# kantime predict
kan_point_predictions <- 
kantime_fit |>
 modeltime_table() |>
  modeltime_forecast(actual_data = training, new_data = testing) 
#> Predicting: |          | 0/? [00:00<?, ?it/s]Predicting:   0%|          | 0/1 [00:00<?, ?it/s]Predicting DataLoader 0:   0%|          | 0/1 [00:00<?, ?it/s]Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 50.65it/s]Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 50.65it/s]

kan_point_predictions |>
  plot_modeltime_forecast(.conf_interval_show = FALSE, .interactive = FALSE)

To Do’s

1. KAN: KAN is still univariate without xreg support. (I wasn’t sure if this would work so started with only bridging a few KAN parameters.)
2. KAN could use more informative info when printed.
3. predict(…type = “prob”), by design, kantime wraps the conformal method of nixtla so prediction intervals could be mapped to predict() calls, by-passing the need to calibrate in R, depending on the situation.
4. add in sample predictions to the modeltime bridge
5. UX: handle nixtla requirements, i.e. y, ds and unique_id, could be tidied within internals
6. UI: pandas/reticulate arg types! internals should handle numeric inputs as integers and pass to python for R programmer.
7. handle warnings like NIXTLA_ID_AS_COL and
8. .nested.col = purrr::map2(...) in final modeltime_forecast