11  Criando modelos customizados com sktime

O sktime oferece um ecossistema robusto, mas em cenarios reais frequentemente precisamos ajustar comportamentos, incorporar dados hierarquicos ou adicionar pre-processamentos especificos. Felizmente, o sktime torna relativamente simples a criacao de modelos customizados, desde que sigamos algumas regras.

Acredito que essa e uma das grandes vantagens da biblioteca: o foco em ser extensivel e customizavel.

11.1 O sistema de tags

Entre a chamada dos metodos publicos e privados existe uma camada de validacoes e conversões controlada pelas tags. Elas sinalizam ao BaseForecaster e ao BaseTransformer o que precisa ser garantido antes de executar a implementacao customizada.

As tags mais importantes para um forecaster podem ser definidas assim:

_tags = {
    "capability:exogenous": True,
    "requires-fh-in-fit": False,
    "X_inner_mtype": [
        "pd.Series",
        "pd.DataFrame",
        "pd-multiindex",
        "pd_multiindex_hier",
    ],
    "y_inner_mtype": [
        "pd.Series",
        "pd.DataFrame",
        "pd-multiindex",
        "pd_multiindex_hier",
    ]
}

Cada uma delas indica o que o modelo é capaz de fazer nos seus métodos privados _fit e _predict:

  • capability:exogenous: Indica se o modelo suporta variáveis exógenas (X) durante o ajuste e a previsão.
  • requires-fh-in-fit: Indica se o modelo precisa do horizonte de previsão (fh) durante o ajuste. Alguns modelos precisam devido a sua implementação interna.
  • y_inner_mtype: Define os tipos de dados aceitos para a variável dependente (y) durante o ajuste e a previsão.
  • X_inner_mtype: Define os tipos de dados aceitos para as variáveis exógenas (X) durante o ajuste e a previsão.

Os machine-types (mtypes), ou tipos para máquina, são a peça mais crucial nesse sistema.

11.1.1 Machine-types disponiveis

Os mtypes definem qual a estrutura de dados que o modelo aceita como entrada e produz como saída. Os principais mtypes para séries temporais são:

  • np.ndarray
  • pd.Series
  • pd.DataFrame
  • pd-multiindex (ideia de painel)
  • pd_multiindex_hier (dados hierarquicos)

Se o modelo suporta um mtype hierárquico e passamos um dado hierárquico, o dado chegará normalmente ao método privado _fit ou _predict. Caso contrário, o sktime tentará converter o dado para um mtype suportado.

11.1.1.1 Baixando exemplos por mtype

Para entender melhor cada mtype, podemos baixar exemplos práticos usando a função get_examples do sktime:

from sktime.datatypes import get_examples

get_examples(mtype="np.ndarray", as_scitype="Series")[0]
array([[ 1. ],
       [ 4. ],
       [ 0.5],
       [-3. ]])
get_examples(mtype="pd.DataFrame", as_scitype="Series")[0].head()
a
0 1.0
1 4.0
2 0.5
3 -3.0 
get_examples(mtype="pd-multiindex", as_scitype="Panel")[0].head()
var_0 var_1
instances timepoints
0 0 1 4
1 2 5
2 3 6
1 0 1 4
1 2 55 
get_examples(mtype="pd_multiindex_hier", as_scitype="Hierarchical")[0].head()
var_0 var_1
foo bar timepoints
a 0 0 1 4
1 2 5
2 3 6
1 0 1 4
1 2 55

Alguns mtypes tem limitações: uma pd.Series simples nao representa problemas hierarquicos, sendo necessario recorrer ao pd_multiindex_hier.

11.1.1.2 Exemplo prático

Vamos criar o nosso primeiro esqueleto de forecaster customizado. Para isso, baixamos uma série de exemplo:

from sktime.forecasting.base import BaseForecaster
from sktime.utils._testing.series import _make_series

y = _make_series(4)
y
2000-01-01    2.455035
2000-01-02    2.462026
2000-01-03    2.065044
2000-01-04    1.000000
Freq: D, dtype: float64

Nosso protótipo irá apenas printar os dados recebidos no método _fit. O __init__ recebe um dicionário de tags para definir as capacidades do modelo.

class Logger(BaseForecaster):

    _tags = {
        "requires-fh-in-fit": False,
    }

    def __init__(self, tags_to_set):
        self.tags_to_set = tags_to_set
        super().__init__()

        self.set_tags(**tags_to_set)
    
    def _fit(self, y, X=None, fh=None):
        print("Inside fit:")
        print(y)
        return self
logger = Logger(tags_to_set={"y_inner_mtype" : ["pd.Series"] })
logger.fit(y)
Inside fit:
2000-01-01    2.455035
2000-01-02    2.462026
2000-01-03    2.065044
2000-01-04    1.000000
Freq: D, dtype: float64
Logger(tags_to_set={'y_inner_mtype': ['pd.Series']})
Please rerun this cell to show the HTML repr or trust the notebook.
logger = Logger(tags_to_set={"y_inner_mtype" : ["np.ndarray"] })
logger.fit(y)
Inside fit:
[[2.45503502]
 [2.46202559]
 [2.06504351]
 [1.        ]]
Logger(tags_to_set={'y_inner_mtype': ['np.ndarray']})
Please rerun this cell to show the HTML repr or trust the notebook.
logger = Logger(tags_to_set={"y_inner_mtype" : ["pd.DataFrame"] })
logger.fit(y)
Inside fit:
                   0
2000-01-01  2.455035
2000-01-02  2.462026
2000-01-03  2.065044
2000-01-04  1.000000
Logger(tags_to_set={'y_inner_mtype': ['pd.DataFrame']})
Please rerun this cell to show the HTML repr or trust the notebook.
try:
    logger = Logger(tags_to_set={"y_inner_mtype" : ["pd_multiindex_hier"] })
    logger.fit(y)
except ValueError as e:
    print(e)
Error in Logger, no series scitypes supported, likely a bug in estimator: scitypes arg passed to _most_complex_scitype are ['Hierarchical']
try:
    logger = Logger(tags_to_set={"y_inner_mtype": ["pd.DataFrame", "pd_multiindex_hier"]})
    logger.fit(y)
except ValueError as e:
    print(e)
Inside fit:
                   0
2000-01-01  2.455035
2000-01-02  2.462026
2000-01-03  2.065044
2000-01-04  1.000000

11.1.1.3 Input hierárquico

Agora veremos como o modelo se comporta com dados hierárquicos. Note que, nos casos onde o modelo não suporta dados hierárquicos, o sktime tentará convertê-los para um mtype suportado.

from sktime.utils._testing.hierarchical import _make_hierarchical

y = _make_hierarchical((1,2), max_timepoints=4, min_timepoints=2)
y
c0
h0 h1 time
h0_0 h1_0 2000-01-02 1.722406
2000-01-03 3.716378
2000-01-04 2.462551
h1_1 2000-01-02 3.211150
2000-01-03 1.000000
2000-01-04 2.024785 
logger = Logger(tags_to_set={"y_inner_mtype" : ["pd.Series"] })
logger.fit(y)
Inside fit:
time
2000-01-02    1.722406
2000-01-03    3.716378
2000-01-04    2.462551
Freq: D, dtype: float64
Inside fit:
time
2000-01-02    3.211150
2000-01-03    1.000000
2000-01-04    2.024785
Freq: D, dtype: float64
Logger(tags_to_set={'y_inner_mtype': ['pd.Series']})
Please rerun this cell to show the HTML repr or trust the notebook.
logger = Logger(tags_to_set={"y_inner_mtype" : ["np.ndarray"] })
logger.fit(y)
Inside fit:
[[1.72240559]
 [3.71637826]
 [2.46255143]]
Inside fit:
[[3.21115029]
 [1.        ]
 [2.02478484]]
Logger(tags_to_set={'y_inner_mtype': ['np.ndarray']})
Please rerun this cell to show the HTML repr or trust the notebook.
logger = Logger(tags_to_set={"y_inner_mtype" : ["pd.DataFrame"] })
logger.fit(y)
Inside fit:
                  c0
time                
2000-01-02  1.722406
2000-01-03  3.716378
2000-01-04  2.462551
Inside fit:
                  c0
time                
2000-01-02  3.211150
2000-01-03  1.000000
2000-01-04  2.024785
Logger(tags_to_set={'y_inner_mtype': ['pd.DataFrame']})
Please rerun this cell to show the HTML repr or trust the notebook.
try:
    logger = Logger(tags_to_set={"y_inner_mtype": ["pd_multiindex_hier"]})
    logger.fit(y)
except ValueError as e:
    print(e)
Inside fit:
                            c0
h0   h1   time                
h0_0 h1_0 2000-01-02  1.722406
          2000-01-03  3.716378
          2000-01-04  2.462551
     h1_1 2000-01-02  3.211150
          2000-01-03  1.000000
          2000-01-04  2.024785

11.2 Criando um modelo naive

Agora, vamos implementar um modelo simples de previsão, o CustomNaiveForecaster, que prevê o valor médio dos últimos n pontos da série temporal.

É um exemplo simples, mas que ilustra bem como criar um forecaster customizado com sktime.

from tsbook.datasets.retail import SyntheticRetail

dataset = SyntheticRetail("panel")
y_train, y_test  = dataset.load("y_train", "y_test")
y_train
sales
sku_id date
0 2020-01-01 0
2020-01-02 0
2020-01-03 0
2020-01-04 0
2020-01-05 0
... ... ...
24 2024-07-01 218
2024-07-02 198
2024-07-03 182
2024-07-04 210
2024-07-05 209

41200 rows × 1 columns

from sktime.utils.plotting import plot_series

plot_series(
    y_train.loc[0],
    y_train.loc[24],
    labels=[
        "SKU 0",
        "SKU 24",
    ],
)

Abaixo, implementamos o CustomNaiveForecaster seguindo as regras do sktime (clique para expandir). Em seguida, explicamos passo a passo.

Code 
from sktime.forecasting.base import BaseForecaster
import pandas as pd


class CustomNaiveForecaster(BaseForecaster):
    """
    A simple naive forecaster

    Parameters
    ----------
    n : int
        Number of past values to use.
    """

    _tags = {
        "requires-fh-in-fit": False,
        "y_inner_mtype": [
            "pd.Series",
        ],
    }

    # Add hyperparameters in init!
    def __init__(self, n=1):
        # 1. Set hyper-parameters
        self.n = n

        # 2. Initialize parent class
        super().__init__()

        # 3. Check hyper-parameters
        assert self.n > 0, "n must be greater than 0"

    def _fit(self, y, X, fh):
        """
        Fit necessary parameters.
        """

        self.value_ = y.iloc[-self.n :].mean()
        return self

    def _predict(self, fh, X):
        """
        Use forecasting horizon and optionally X to predict y
        """

        # During fit, BaseForecaster sets
        # self.cutoff to the latest cutoff time point
        index = fh.to_absolute_index(self.cutoff)
        y_pred = pd.Series(
            index=index,
            data=[self.value_ for _ in range(len(index))],
        )
        y_pred.name = self._y.name

        return y_pred

    # Veremos mais tarde como usar esse método
    @classmethod
    def get_test_params(cls, parameter_set="default"):
        return [
            {"n": 1},
            {"n": 2},
        ]

11.2.1 Definindo o método __init__

O método __init__ possui 3 etapas:

  1. A definição dos hiperparametros e seus atributos com mesmo nome.
  2. A chamada do super().__init__() para inicializar a classe pai.
  3. A validação dos hiperparâmetros.
# Add hyperparameters in init!
def __init__(self, n=1):
    # 1. Set hyper-parameters
    self.n = n

    # 2. Initialize parent class
    super().__init__()

    # 3. Check hyper-parameters
    assert self.n > 0, "n must be greater than 0"

No caso de algum preprocessamento dos hiperparâmetros no __init__, devemos guardar em uma variável com nome diferente do hiperparâmetro. Por exemplo, se tivéssemos interesse em ter um atributo n diferente do passado no __init__, poderíamos fazer:

self._n = n + 1

O self.n funciona como uma digital do modelo, e deve ser exatamente o que foi passado no __init__.

11.2.2 Definindo o método _fit

No método _fit, devemos implementar a lógica de ajuste do modelo. No nosso caso, calculamos a média dos últimos n valores e armazenamos em self.value_.

O _ após o nome do atributo indica que é um atributo aprendido durante o ajuste, e será retornado quando chamarmos get_fitted_params().

Note que podemos supor que y é do tipo definido na tag y_inner_mtype, ou seja, uma pd.Series.

11.2.3 Definindo o método _predict

No método _predict, implementamos a lógica de previsão. Usamos o horizonte de previsão fh para determinar os índices futuros e retornamos uma série com o valor previsto para cada ponto no horizonte.

O fh é um objeto do tipo ForecastingHorizon, que possui o método to_absolute_index(cutoff) para converter o horizonte relativo em índices absolutos, considerando o último ponto conhecido (self.cutoff).

Retornamos um pd.Series com os índices e os valores previstos.

11.2.4 Usando o CustomNaiveForecaster

Agora, já podemos usar o nosso modelo customizado para fazer previsões.

custom_naive_model = CustomNaiveForecaster()
custom_naive_model.fit(y_train)
CustomNaiveForecaster()
Please rerun this cell to show the HTML repr or trust the notebook.

Como passamos um dado hierárquico, o sktime converteu automaticamente para pd.Series, que é o mtype suportado pelo nosso modelo. Os modelos internos, para cada série, ficam disponíveis em forecasters_.

custom_naive_model.forecasters_
forecasters
0 CustomNaiveForecaster()
1 CustomNaiveForecaster()
2 CustomNaiveForecaster()
3 CustomNaiveForecaster()
4 CustomNaiveForecaster()
5 CustomNaiveForecaster()
6 CustomNaiveForecaster()
7 CustomNaiveForecaster()
8 CustomNaiveForecaster()
9 CustomNaiveForecaster()
10 CustomNaiveForecaster()
11 CustomNaiveForecaster()
12 CustomNaiveForecaster()
13 CustomNaiveForecaster()
14 CustomNaiveForecaster()
15 CustomNaiveForecaster()
16 CustomNaiveForecaster()
17 CustomNaiveForecaster()
18 CustomNaiveForecaster()
19 CustomNaiveForecaster()
20 CustomNaiveForecaster()
21 CustomNaiveForecaster()
22 CustomNaiveForecaster()
23 CustomNaiveForecaster()
24 CustomNaiveForecaster() 
y_pred = custom_naive_model.predict(fh=y_test.index.get_level_values(-1).unique())

fig, _ = plot_series(
    y_train.loc[0],
    y_pred.loc[0],
    labels=[
        "SKU 0",
        "Previsão SKU 0",
    ],
)
fig.show()

11.3 Testes unitários

O sktime também fornece uma funcionalidade que traz testes unitários prontos para validar se o modelo customizado está funcionando corretamente.

Ele usa os hiperparâmetros retornados pelo método get_test_params para criar instâncias do modelo e executar uma série de testes.

from sktime.utils.estimator_checks import check_estimator


check_estimator(CustomNaiveForecaster, tests_to_exclude=["test_doctest_examples"])
All tests PASSED!
{'test_inheritance[CustomNaiveForecaster]': 'PASSED',
 'test_set_params_sklearn[CustomNaiveForecaster]': 'PASSED',
 'test_no_between_test_case_side_effects[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-0]': 'PASSED',
 'test_no_between_test_case_side_effects[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-1]': 'PASSED',
 'test_no_between_test_case_side_effects[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-0]': 'PASSED',
 'test_no_between_test_case_side_effects[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-1]': 'PASSED',
 'test_no_between_test_case_side_effects[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-0]': 'PASSED',
 'test_no_between_test_case_side_effects[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-1]': 'PASSED',
 'test_no_between_test_case_side_effects[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-0]': 'PASSED',
 'test_no_between_test_case_side_effects[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-1]': 'PASSED',
 'test_obj_vs_cls_signature[CustomNaiveForecaster]': 'PASSED',
 'test_repr[CustomNaiveForecaster-0]': 'PASSED',
 'test_repr[CustomNaiveForecaster-1]': 'PASSED',
 'test_estimator_tags[CustomNaiveForecaster]': 'PASSED',
 'test_get_params[CustomNaiveForecaster-0]': 'PASSED',
 'test_get_params[CustomNaiveForecaster-1]': 'PASSED',
 'test_random_tags[CustomNaiveForecaster]': 'PASSED',
 'test_create_test_instances_and_names[CustomNaiveForecaster]': 'PASSED',
 'test_get_test_params[CustomNaiveForecaster]': 'PASSED',
 'test_get_test_params_coverage[CustomNaiveForecaster]': 'PASSED',
 'test_clone[CustomNaiveForecaster-0]': 'PASSED',
 'test_clone[CustomNaiveForecaster-1]': 'PASSED',
 'test_no_cross_test_side_effects_part1[CustomNaiveForecaster-0]': 'PASSED',
 'test_no_cross_test_side_effects_part1[CustomNaiveForecaster-1]': 'PASSED',
 'test_set_params[CustomNaiveForecaster-0]': 'PASSED',
 'test_set_params[CustomNaiveForecaster-1]': 'PASSED',
 'test_valid_estimator_tags[CustomNaiveForecaster-0]': 'PASSED',
 'test_valid_estimator_tags[CustomNaiveForecaster-1]': 'PASSED',
 'test_has_common_interface[CustomNaiveForecaster]': 'PASSED',
 'test_repr_html[CustomNaiveForecaster-0]': 'PASSED',
 'test_repr_html[CustomNaiveForecaster-1]': 'PASSED',
 'test_no_cross_test_side_effects_part2[CustomNaiveForecaster-0]': 'PASSED',
 'test_no_cross_test_side_effects_part2[CustomNaiveForecaster-1]': 'PASSED',
 'test_create_test_instance[CustomNaiveForecaster]': 'PASSED',
 'test_valid_estimator_class_tags[CustomNaiveForecaster]': 'PASSED',
 'test_constructor[CustomNaiveForecaster]': 'PASSED',
 'test_non_state_changing_method_contract[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',
 'test_non_state_changing_method_contract[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-get_fitted_params]': 'PASSED',
 'test_non_state_changing_method_contract[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',
 'test_non_state_changing_method_contract[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-get_fitted_params]': 'PASSED',
 'test_non_state_changing_method_contract[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',
 'test_non_state_changing_method_contract[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-get_fitted_params]': 'PASSED',
 'test_non_state_changing_method_contract[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',
 'test_non_state_changing_method_contract[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-get_fitted_params]': 'PASSED',
 'test_fit_returns_self[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX]': 'PASSED',
 'test_fit_returns_self[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX]': 'PASSED',
 'test_fit_returns_self[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX]': 'PASSED',
 'test_fit_returns_self[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX]': 'PASSED',
 'test_persistence_via_pickle[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',
 'test_persistence_via_pickle[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',
 'test_persistence_via_pickle[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',
 'test_persistence_via_pickle[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',
 'test_raises_not_fitted_error[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',
 'test_raises_not_fitted_error[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-get_fitted_params]': 'PASSED',
 'test_raises_not_fitted_error[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',
 'test_raises_not_fitted_error[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-get_fitted_params]': 'PASSED',
 'test_raises_not_fitted_error[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',
 'test_raises_not_fitted_error[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-get_fitted_params]': 'PASSED',
 'test_raises_not_fitted_error[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',
 'test_raises_not_fitted_error[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-get_fitted_params]': 'PASSED',
 'test_dl_constructor_initializes_deeply[CustomNaiveForecaster]': 'PASSED',
 'test_fit_updates_state[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX]': 'PASSED',
 'test_fit_updates_state[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX]': 'PASSED',
 'test_fit_updates_state[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX]': 'PASSED',
 'test_fit_updates_state[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX]': 'PASSED',
 'test_fit_idempotent[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',
 'test_fit_idempotent[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',
 'test_fit_idempotent[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',
 'test_fit_idempotent[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',
 'test_methods_have_no_side_effects[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',
 'test_methods_have_no_side_effects[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-get_fitted_params]': 'PASSED',
 'test_methods_have_no_side_effects[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',
 'test_methods_have_no_side_effects[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-get_fitted_params]': 'PASSED',
 'test_methods_have_no_side_effects[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',
 'test_methods_have_no_side_effects[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-get_fitted_params]': 'PASSED',
 'test_methods_have_no_side_effects[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',
 'test_methods_have_no_side_effects[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-get_fitted_params]': 'PASSED',
 'test_multiprocessing_idempotent[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',
 'test_multiprocessing_idempotent[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',
 'test_multiprocessing_idempotent[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',
 'test_multiprocessing_idempotent[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',
 'test_fit_does_not_overwrite_hyper_params[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX]': 'PASSED',
 'test_fit_does_not_overwrite_hyper_params[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX]': 'PASSED',
 'test_fit_does_not_overwrite_hyper_params[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX]': 'PASSED',
 'test_fit_does_not_overwrite_hyper_params[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX]': 'PASSED',
 'test_save_estimators_to_file[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',
 'test_save_estimators_to_file[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',
 'test_save_estimators_to_file[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',
 'test_save_estimators_to_file[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',
 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=1-int-int-True]': 'PASSED',
 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=1-int-int-False]': 'PASSED',
 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=1-range-int-True]': 'PASSED',
 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=1-range-int-False]': 'PASSED',
 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=1-period-int-True]': 'PASSED',
 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=1-period-period-False]': 'PASSED',
 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=1-datetime-int-True]': 'PASSED',
 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=1-datetime-datetime-False]': 'PASSED',
 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=1-datetime-timedelta-True]': 'PASSED',
 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-int-int-True]': 'PASSED',
 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-int-int-False]': 'PASSED',
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 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-period-int-True]': 'PASSED',
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 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-datetime-datetime-False]': 'PASSED',
 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-datetime-timedelta-True]': 'PASSED',
 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:2cols-fh=1-int-int-True]': 'PASSED',
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 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-datetime-timedelta-True]': 'PASSED',
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 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-datetime-timedelta-True]': 'PASSED',
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 'test_predict_interval[CustomNaiveForecaster-0-y:1cols-fh=1-alpha=[0.25, 0.75]-1]': 'PASSED',
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 'test_predict_interval[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-alpha=0.05-1]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-alpha=0.1-0]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-alpha=0.1-1]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-alpha=[0.25, 0.75]-0]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-alpha=[0.25, 0.75]-1]': 'PASSED',
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 'test_predict_interval[CustomNaiveForecaster-0-y:2cols-fh=1-alpha=[0.25, 0.75]-0]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-0-y:2cols-fh=1-alpha=[0.25, 0.75]-1]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-alpha=0.05-0]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-alpha=0.05-1]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-alpha=0.1-0]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-alpha=0.1-1]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-alpha=[0.25, 0.75]-0]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-alpha=[0.25, 0.75]-1]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-1-y:1cols-fh=1-alpha=0.05-0]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-1-y:1cols-fh=1-alpha=0.05-1]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-1-y:1cols-fh=1-alpha=0.1-0]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-1-y:1cols-fh=1-alpha=0.1-1]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-1-y:1cols-fh=1-alpha=[0.25, 0.75]-0]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-1-y:1cols-fh=1-alpha=[0.25, 0.75]-1]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-alpha=0.05-0]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-alpha=0.05-1]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-alpha=0.1-0]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-alpha=0.1-1]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-alpha=[0.25, 0.75]-0]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-alpha=[0.25, 0.75]-1]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-1-y:2cols-fh=1-alpha=0.05-0]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-1-y:2cols-fh=1-alpha=0.05-1]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-1-y:2cols-fh=1-alpha=0.1-0]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-1-y:2cols-fh=1-alpha=0.1-1]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-1-y:2cols-fh=1-alpha=[0.25, 0.75]-0]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-1-y:2cols-fh=1-alpha=[0.25, 0.75]-1]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-alpha=0.05-0]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-alpha=0.05-1]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-alpha=0.1-0]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-alpha=0.1-1]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-alpha=[0.25, 0.75]-0]': 'PASSED',
 'test_predict_interval[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-alpha=[0.25, 0.75]-1]': 'PASSED',
 'test_fh_not_passed_error_handling[CustomNaiveForecaster-0-y:1cols]': 'PASSED',
 'test_fh_not_passed_error_handling[CustomNaiveForecaster-0-y:2cols]': 'PASSED',
 'test_fh_not_passed_error_handling[CustomNaiveForecaster-1-y:1cols]': 'PASSED',
 'test_fh_not_passed_error_handling[CustomNaiveForecaster-1-y:2cols]': 'PASSED',
 'test_fit_predict[CustomNaiveForecaster-0-y:1cols]': 'PASSED',
 'test_fit_predict[CustomNaiveForecaster-0-y:2cols]': 'PASSED',
 'test_fit_predict[CustomNaiveForecaster-1-y:1cols]': 'PASSED',
 'test_fit_predict[CustomNaiveForecaster-1-y:2cols]': 'PASSED',
 'test_categorical_X_raises_error[CustomNaiveForecaster-0]': 'PASSED',
 'test_categorical_X_raises_error[CustomNaiveForecaster-1]': 'PASSED',
 'test_raises_not_fitted_error[CustomNaiveForecaster-0]': 'PASSED',
 'test_raises_not_fitted_error[CustomNaiveForecaster-1]': 'PASSED',
 'test__y_when_refitting[CustomNaiveForecaster-0-y:1cols]': 'PASSED',
 'test__y_when_refitting[CustomNaiveForecaster-0-y:2cols]': 'PASSED',
 'test__y_when_refitting[CustomNaiveForecaster-1-y:1cols]': 'PASSED',
 'test__y_when_refitting[CustomNaiveForecaster-1-y:2cols]': 'PASSED',
 'test_predict_series_name_preserved[CustomNaiveForecaster-0]': 'PASSED',
 'test_predict_series_name_preserved[CustomNaiveForecaster-1]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:1cols-int-int-True]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:1cols-int-int-False]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:1cols-range-int-True]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:1cols-range-int-False]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:1cols-period-int-True]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:1cols-period-period-False]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:1cols-datetime-int-True]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:1cols-datetime-datetime-False]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:1cols-datetime-timedelta-True]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:2cols-int-int-True]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:2cols-int-int-False]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:2cols-range-int-True]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:2cols-range-int-False]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:2cols-period-int-True]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:2cols-period-period-False]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:2cols-datetime-int-True]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:2cols-datetime-datetime-False]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:2cols-datetime-timedelta-True]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:1cols-int-int-True]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:1cols-int-int-False]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:1cols-range-int-True]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:1cols-range-int-False]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:1cols-period-int-True]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:1cols-period-period-False]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:1cols-datetime-int-True]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:1cols-datetime-datetime-False]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:1cols-datetime-timedelta-True]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:2cols-int-int-True]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:2cols-int-int-False]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:2cols-range-int-True]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:2cols-range-int-False]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:2cols-period-int-True]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:2cols-period-period-False]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:2cols-datetime-int-True]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:2cols-datetime-datetime-False]': 'PASSED',
 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:2cols-datetime-timedelta-True]': 'PASSED',
 'test_predict_quantiles[CustomNaiveForecaster-0-y:1cols-fh=1-alpha=0.05]': 'PASSED',
 'test_predict_quantiles[CustomNaiveForecaster-0-y:1cols-fh=1-alpha=0.1]': 'PASSED',
 'test_predict_quantiles[CustomNaiveForecaster-0-y:1cols-fh=1-alpha=[0.25, 0.75]]': 'PASSED',
 'test_predict_quantiles[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-alpha=0.05]': 'PASSED',
 'test_predict_quantiles[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-alpha=0.1]': 'PASSED',
 'test_predict_quantiles[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-alpha=[0.25, 0.75]]': 'PASSED',
 'test_predict_quantiles[CustomNaiveForecaster-0-y:2cols-fh=1-alpha=0.05]': 'PASSED',
 'test_predict_quantiles[CustomNaiveForecaster-0-y:2cols-fh=1-alpha=0.1]': 'PASSED',
 'test_predict_quantiles[CustomNaiveForecaster-0-y:2cols-fh=1-alpha=[0.25, 0.75]]': 'PASSED',
 'test_predict_quantiles[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-alpha=0.05]': 'PASSED',
 'test_predict_quantiles[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-alpha=0.1]': 'PASSED',
 'test_predict_quantiles[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-alpha=[0.25, 0.75]]': 'PASSED',
 'test_predict_quantiles[CustomNaiveForecaster-1-y:1cols-fh=1-alpha=0.05]': 'PASSED',
 'test_predict_quantiles[CustomNaiveForecaster-1-y:1cols-fh=1-alpha=0.1]': 'PASSED',
 'test_predict_quantiles[CustomNaiveForecaster-1-y:1cols-fh=1-alpha=[0.25, 0.75]]': 'PASSED',
 'test_predict_quantiles[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-alpha=0.05]': 'PASSED',
 'test_predict_quantiles[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-alpha=0.1]': 'PASSED',
 'test_predict_quantiles[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-alpha=[0.25, 0.75]]': 'PASSED',
 'test_predict_quantiles[CustomNaiveForecaster-1-y:2cols-fh=1-alpha=0.05]': 'PASSED',
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 'test_predict_quantiles[CustomNaiveForecaster-1-y:2cols-fh=1-alpha=[0.25, 0.75]]': 'PASSED',
 'test_predict_quantiles[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-alpha=0.05]': 'PASSED',
 'test_predict_quantiles[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-alpha=0.1]': 'PASSED',
 'test_predict_quantiles[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-alpha=[0.25, 0.75]]': 'PASSED',
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 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=1-datetime-int-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=1-datetime-datetime-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=1-datetime-timedelta-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-int-int-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-int-int-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-range-int-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-range-int-False]': 'PASSED',
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 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-datetime-int-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-datetime-datetime-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-datetime-timedelta-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=-3-int-int-True]': 'PASSED',
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 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=-3-datetime-int-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=-3-datetime-datetime-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=-3-datetime-timedelta-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[-2 -5]-int-int-True]': 'PASSED',
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 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=0-datetime-datetime-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=0-datetime-timedelta-True]': 'PASSED',
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 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=1-datetime-timedelta-True]': 'PASSED',
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 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-int-int-True]': 'PASSED',
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 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-period-int-True]': 'PASSED',
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 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-datetime-int-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-datetime-datetime-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-datetime-timedelta-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=-3-int-int-True]': 'PASSED',
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 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=-3-datetime-datetime-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=-3-datetime-timedelta-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[-2 -5]-int-int-True]': 'PASSED',
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 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[-2 -5]-datetime-datetime-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[-2 -5]-datetime-timedelta-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=0-int-int-True]': 'PASSED',
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 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=0-range-int-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=0-period-int-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=0-period-period-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=0-datetime-int-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=0-datetime-datetime-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=0-datetime-timedelta-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[-3  2]-int-int-True]': 'PASSED',
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 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[-3  2]-datetime-datetime-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[-3  2]-datetime-timedelta-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=1-int-int-True]': 'PASSED',
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 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=1-range-int-False]': 'PASSED',
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 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=1-datetime-int-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=1-datetime-datetime-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=1-datetime-timedelta-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-int-int-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-int-int-False]': 'PASSED',
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 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-range-int-False]': 'PASSED',
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 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-period-period-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-datetime-int-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-datetime-datetime-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-datetime-timedelta-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=-3-int-int-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=-3-int-int-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=-3-range-int-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=-3-range-int-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=-3-period-int-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=-3-period-period-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=-3-datetime-int-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=-3-datetime-datetime-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=-3-datetime-timedelta-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-2 -5]-int-int-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-2 -5]-int-int-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-2 -5]-range-int-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-2 -5]-range-int-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-2 -5]-period-int-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-2 -5]-period-period-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-2 -5]-datetime-int-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-2 -5]-datetime-datetime-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-2 -5]-datetime-timedelta-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=0-int-int-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=0-int-int-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=0-range-int-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=0-range-int-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=0-period-int-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=0-period-period-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=0-datetime-int-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=0-datetime-datetime-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=0-datetime-timedelta-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-3  2]-int-int-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-3  2]-int-int-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-3  2]-range-int-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-3  2]-range-int-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-3  2]-period-int-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-3  2]-period-period-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-3  2]-datetime-int-True]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-3  2]-datetime-datetime-False]': 'PASSED',
 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-3  2]-datetime-timedelta-True]': 'PASSED',
 'test_update_with_exogenous_variables[CustomNaiveForecaster-0-y:1cols-update_params=True]': 'PASSED',
 'test_update_with_exogenous_variables[CustomNaiveForecaster-0-y:1cols-update_params=False]': 'PASSED',
 'test_update_with_exogenous_variables[CustomNaiveForecaster-0-y:2cols-update_params=True]': 'PASSED',
 'test_update_with_exogenous_variables[CustomNaiveForecaster-0-y:2cols-update_params=False]': 'PASSED',
 'test_update_with_exogenous_variables[CustomNaiveForecaster-1-y:1cols-update_params=True]': 'PASSED',
 'test_update_with_exogenous_variables[CustomNaiveForecaster-1-y:1cols-update_params=False]': 'PASSED',
 'test_update_with_exogenous_variables[CustomNaiveForecaster-1-y:2cols-update_params=True]': 'PASSED',
 'test_update_with_exogenous_variables[CustomNaiveForecaster-1-y:2cols-update_params=False]': 'PASSED',
 'test_hierarchical_with_exogeneous[CustomNaiveForecaster-0-y:1cols]': 'PASSED',
 'test_hierarchical_with_exogeneous[CustomNaiveForecaster-0-y:2cols]': 'PASSED',
 'test_hierarchical_with_exogeneous[CustomNaiveForecaster-1-y:1cols]': 'PASSED',
 'test_hierarchical_with_exogeneous[CustomNaiveForecaster-1-y:2cols]': 'PASSED',
 'test_score[CustomNaiveForecaster-0-y:1cols-fh=1]': 'PASSED',
 'test_score[CustomNaiveForecaster-0-y:1cols-fh=[2 5]]': 'PASSED',
 'test_score[CustomNaiveForecaster-0-y:2cols-fh=1]': 'PASSED',
 'test_score[CustomNaiveForecaster-0-y:2cols-fh=[2 5]]': 'PASSED',
 'test_score[CustomNaiveForecaster-1-y:1cols-fh=1]': 'PASSED',
 'test_score[CustomNaiveForecaster-1-y:1cols-fh=[2 5]]': 'PASSED',
 'test_score[CustomNaiveForecaster-1-y:2cols-fh=1]': 'PASSED',
 'test_score[CustomNaiveForecaster-1-y:2cols-fh=[2 5]]': 'PASSED',
 'test_different_fh_in_fit_and_predict_error_handling[CustomNaiveForecaster-0-y:1cols]': 'PASSED',
 'test_different_fh_in_fit_and_predict_error_handling[CustomNaiveForecaster-0-y:2cols]': 'PASSED',
 'test_different_fh_in_fit_and_predict_error_handling[CustomNaiveForecaster-1-y:1cols]': 'PASSED',
 'test_different_fh_in_fit_and_predict_error_handling[CustomNaiveForecaster-1-y:2cols]': 'PASSED',
 'test_y_invalid_type_raises_error[CustomNaiveForecaster-0-0]': 'PASSED',
 'test_y_invalid_type_raises_error[CustomNaiveForecaster-0-1]': 'PASSED',
 'test_y_invalid_type_raises_error[CustomNaiveForecaster-1-0]': 'PASSED',
 'test_y_invalid_type_raises_error[CustomNaiveForecaster-1-1]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:1cols-update_params=True-0-fh=1]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:1cols-update_params=True-0-fh=[2 5]]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:1cols-update_params=True-1-fh=1]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:1cols-update_params=True-1-fh=[2 5]]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:1cols-update_params=False-step=1-0-fh=1]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:1cols-update_params=False-step=1-0-fh=[2 5]]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:1cols-update_params=False-step=1-1-fh=1]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:1cols-update_params=False-step=1-1-fh=[2 5]]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:1cols-update_params=False-step=5-0-fh=1]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:1cols-update_params=False-step=5-0-fh=[2 5]]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:1cols-update_params=False-step=5-1-fh=1]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:1cols-update_params=False-step=5-1-fh=[2 5]]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:2cols-update_params=True-0-fh=1]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:2cols-update_params=True-0-fh=[2 5]]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:2cols-update_params=True-1-fh=1]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:2cols-update_params=True-1-fh=[2 5]]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:2cols-update_params=False-step=1-0-fh=1]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:2cols-update_params=False-step=1-0-fh=[2 5]]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:2cols-update_params=False-step=1-1-fh=1]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:2cols-update_params=False-step=1-1-fh=[2 5]]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:2cols-update_params=False-step=5-0-fh=1]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:2cols-update_params=False-step=5-0-fh=[2 5]]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:2cols-update_params=False-step=5-1-fh=1]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:2cols-update_params=False-step=5-1-fh=[2 5]]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:1cols-update_params=True-0-fh=1]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:1cols-update_params=True-0-fh=[2 5]]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:1cols-update_params=True-1-fh=1]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:1cols-update_params=True-1-fh=[2 5]]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:1cols-update_params=False-step=1-0-fh=1]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:1cols-update_params=False-step=1-0-fh=[2 5]]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:1cols-update_params=False-step=1-1-fh=1]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:1cols-update_params=False-step=1-1-fh=[2 5]]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:1cols-update_params=False-step=5-0-fh=1]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:1cols-update_params=False-step=5-0-fh=[2 5]]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:1cols-update_params=False-step=5-1-fh=1]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:1cols-update_params=False-step=5-1-fh=[2 5]]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:2cols-update_params=True-0-fh=1]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:2cols-update_params=True-0-fh=[2 5]]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:2cols-update_params=True-1-fh=1]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:2cols-update_params=True-1-fh=[2 5]]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:2cols-update_params=False-step=1-0-fh=1]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:2cols-update_params=False-step=1-0-fh=[2 5]]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:2cols-update_params=False-step=1-1-fh=1]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:2cols-update_params=False-step=1-1-fh=[2 5]]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:2cols-update_params=False-step=5-0-fh=1]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:2cols-update_params=False-step=5-0-fh=[2 5]]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:2cols-update_params=False-step=5-1-fh=1]': 'PASSED',
 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:2cols-update_params=False-step=5-1-fh=[2 5]]': 'PASSED',
 'test_X_invalid_type_raises_error[CustomNaiveForecaster-0-y:1cols-0]': 'PASSED',
 'test_X_invalid_type_raises_error[CustomNaiveForecaster-0-y:1cols-1]': 'PASSED',
 'test_X_invalid_type_raises_error[CustomNaiveForecaster-0-y:2cols-0]': 'PASSED',
 'test_X_invalid_type_raises_error[CustomNaiveForecaster-0-y:2cols-1]': 'PASSED',
 'test_X_invalid_type_raises_error[CustomNaiveForecaster-1-y:1cols-0]': 'PASSED',
 'test_X_invalid_type_raises_error[CustomNaiveForecaster-1-y:1cols-1]': 'PASSED',
 'test_X_invalid_type_raises_error[CustomNaiveForecaster-1-y:2cols-0]': 'PASSED',
 'test_X_invalid_type_raises_error[CustomNaiveForecaster-1-y:2cols-1]': 'PASSED',
 'test_pred_int_tag[CustomNaiveForecaster-0]': 'PASSED',
 'test_pred_int_tag[CustomNaiveForecaster-1]': 'PASSED',
 'test_y_multivariate_raises_error[CustomNaiveForecaster-0]': 'PASSED',
 'test_y_multivariate_raises_error[CustomNaiveForecaster-1]': 'PASSED',
 'test_get_fitted_params[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX]': 'PASSED',
 'test_get_fitted_params[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX]': 'PASSED',
 'test_get_fitted_params[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX]': 'PASSED',
 'test_get_fitted_params[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX]': 'PASSED',
 'test_update_predict_single[CustomNaiveForecaster-0-y:1cols-update_params=True-fh=1]': 'PASSED',
 'test_update_predict_single[CustomNaiveForecaster-0-y:1cols-update_params=True-fh=[2 5]]': 'PASSED',
 'test_update_predict_single[CustomNaiveForecaster-0-y:1cols-update_params=False-fh=1]': 'PASSED',
 'test_update_predict_single[CustomNaiveForecaster-0-y:1cols-update_params=False-fh=[2 5]]': 'PASSED',
 'test_update_predict_single[CustomNaiveForecaster-0-y:2cols-update_params=True-fh=1]': 'PASSED',
 'test_update_predict_single[CustomNaiveForecaster-0-y:2cols-update_params=True-fh=[2 5]]': 'PASSED',
 'test_update_predict_single[CustomNaiveForecaster-0-y:2cols-update_params=False-fh=1]': 'PASSED',
 'test_update_predict_single[CustomNaiveForecaster-0-y:2cols-update_params=False-fh=[2 5]]': 'PASSED',
 'test_update_predict_single[CustomNaiveForecaster-1-y:1cols-update_params=True-fh=1]': 'PASSED',
 'test_update_predict_single[CustomNaiveForecaster-1-y:1cols-update_params=True-fh=[2 5]]': 'PASSED',
 'test_update_predict_single[CustomNaiveForecaster-1-y:1cols-update_params=False-fh=1]': 'PASSED',
 'test_update_predict_single[CustomNaiveForecaster-1-y:1cols-update_params=False-fh=[2 5]]': 'PASSED',
 'test_update_predict_single[CustomNaiveForecaster-1-y:2cols-update_params=True-fh=1]': 'PASSED',
 'test_update_predict_single[CustomNaiveForecaster-1-y:2cols-update_params=True-fh=[2 5]]': 'PASSED',
 'test_update_predict_single[CustomNaiveForecaster-1-y:2cols-update_params=False-fh=1]': 'PASSED',
 'test_update_predict_single[CustomNaiveForecaster-1-y:2cols-update_params=False-fh=[2 5]]': 'PASSED',
 'test__y_and_cutoff[CustomNaiveForecaster-0-y:1cols]': 'PASSED',
 'test__y_and_cutoff[CustomNaiveForecaster-0-y:2cols]': 'PASSED',
 'test__y_and_cutoff[CustomNaiveForecaster-1-y:1cols]': 'PASSED',
 'test__y_and_cutoff[CustomNaiveForecaster-1-y:2cols]': 'PASSED',
 'test_categorical_y_raises_error[CustomNaiveForecaster-0]': 'PASSED',
 'test_categorical_y_raises_error[CustomNaiveForecaster-1]': 'PASSED'}