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BigCodeBench / BigCodeBench v0.1.0_hf 2e0f36fe-7158-5201-9352-e7d25b262fe5

Problem

Answer published by the source. Consult the official source to check your work against its answer.

complete prompt

import pandas as pd
import numpy as np
from statsmodels.tsa.seasonal import seasonal_decompose

def task_func(start_date='2016-01-01', periods=24, freq='M', model='additive'):
    """
    Generate a sales time-series and decompose it into trend, seasonal, and residual components.
    
    Parameters:
    - start_date (str): The start date of the time-series in the format 'YYYY-MM-DD'. Default is '2016-01-01'.
    - periods (int): The number of periods to generate for the time-series. Default is 24.
    - freq (str): The frequency of the time-series data. Default is 'M' (Monthly End).
    - model (str): The type of seasonal decomposition ('additive' or 'multiplicative'). Default is 'additive'.

    Returns:
    - A dictionary containing 'trend', 'seasonal', and 'residual' components as Pandas Series.
    
    Requirements:
    - numpy
    - pandas
    - statsmodels
    
    Examples:
    >>> result = task_func('2016-01-01', 24, 'M')
    >>> all(key in result for key in ['trend', 'seasonal', 'residual'])
    True

    >>> result = task_func('2020-01-01', 24, 'M', 'multiplicative')
    >>> len(result['seasonal'])
    24
    """

instruct prompt

Generate a sales time-series and decompose it into trend, seasonal, and residual components. >>> result = task_func('2020-01-01', 24, 'M', 'multiplicative') >>> len(result['seasonal']) 24
The function should output with:
    A dictionary containing 'trend', 'seasonal', and 'residual' components as Pandas Series.
You should write self-contained code starting with:

Code

import pandas as pd
import numpy as np
from statsmodels.tsa.seasonal import seasonal_decompose
def task_func(start_date='2016-01-01', periods=24, freq='M', model='additive'):

code prompt

Code

import pandas as pd
import numpy as np
from statsmodels.tsa.seasonal import seasonal_decompose
def task_func(start_date='2016-01-01', periods=24, freq='M', model='additive'):

entry point

task_func

libs

  • pandas
  • numpy
  • statsmodels

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Source and history

task_id
2e0f36fe-7158-5201-9352-e7d25b262fe5
task_key
default--v0~2e1~2e0~5fhf--2e0f36fe-7158-5201-9352-e7d25b262fe5
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import