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{"label":"Pylint","message":"10.00","schemaVersion":1,"color":"brightgreen"}

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import pandas as pd
from datasetsforecast.m3 import M3
from neuralforecast.models import NHITS
from neuralforecast import NeuralForecast
from neuralforecast.losses.pytorch import MQLoss, DistributionLoss, PMM, GMM, NBMM

df, _, _ = M3.load(directory='./', group='Monthly')
df['ds'] = pd.to_datetime(df['ds'])

# number of horizons

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from typing import List, Generator

import numpy as np

from sklearn.model_selection._split import _BaseKFold
from sklearn.utils.validation import indexable, _num_samples


class MonteCarloCV(_BaseKFold):
    def __init__(self,

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import numpy as np
import pandas as pd

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

# reading the time series (pd.Series format)
tseries = pd.read_csv('path_to_data.csv') 
# you can simulate some data with:
# tseries = pd.Series(np.random.random(100))

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import numpy as np
from sklearn.neighbors import KernelDensity
from sklearn.base import BaseEstimator, TransformerMixin

from vest.preprocess.embedding import embed2seq, embed


class KDE(BaseEstimator, TransformerMixin):
    """ Transformation based on Kernel Density Estimation
    """

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Lecture 1: Introduction to Research — [📝Lecture Notebooks] [▶️Video]
Lecture 2: Introduction to Python — [📝Lecture Notebooks] [▶️Video]
Lecture 3: Introduction to NumPy — [📝Lecture Notebooks] [▶️Video]
Lecture 4: Introduction to pandas — [📝Lecture Notebooks] [▶️Video]
Lecture 5: Plotting Data — [📝Lecture Notebooks] [[▶️Vide