Data

ts_stat_tests.utils.data

Summary

This module contains utility functions to load classic time series datasets for testing and demonstration purposes.

It provides interfaces for both synthetic data generation (random numbers, sine waves, trends) and external data loading from common benchmarks.

SEED module-attribute

SEED: int = 42

get_random_generator cached

get_random_generator(seed: int) -> RandomGenerator

Summary

Generates a NumPy random number generator with a specified seed for reproducibility.

Details

This function returns a numpy.random.Generator instance using default_rng. This is the recommended way to generate random numbers in modern NumPy (v1.17+).

Parameters:

Name	Type	Description	Default
seed	int	The seed value for the random number generator.	required

Returns:

Type	Description
Generator	A NumPy random number generator initialized with the given seed.

Examples

>>> from ts_stat_tests.utils.data import get_random_generator

>>> rng = get_random_generator(42)
>>> print(rng is not None)
True
>>> print(type(rng))
<class 'numpy.random._generator.Generator'>

References

1. NumPy Random Generator

Source code in src/ts_stat_tests/utils/data.py

@lru_cache
@typechecked
def get_random_generator(seed: int) -> RandomGenerator:
    r"""
    !!! note "Summary"
        Generates a NumPy random number generator with a specified seed for reproducibility.

    ???+ abstract "Details"
        This function returns a `numpy.random.Generator` instance using `default_rng`. This is the recommended way to generate random numbers in modern NumPy (v1.17+).

    Params:
        seed (int):
            The seed value for the random number generator.

    Returns:
        (RandomGenerator):
            A NumPy random number generator initialized with the given seed.

    ???+ example "Examples"

        ```pycon {.py .python linenums="1" title="Setup"}
        >>> from ts_stat_tests.utils.data import get_random_generator

        ```

        ```pycon {.py .python linenums="1" title="Example 1: Creating a RandomGenerator"}
        >>> rng = get_random_generator(42)
        >>> print(rng is not None)
        True
        >>> print(type(rng))
        <class 'numpy.random._generator.Generator'>

        ```

    ??? question "References"
        1. [NumPy Random Generator](https://numpy.org/doc/stable/reference/random/generator.html)

    """
    return np.random.default_rng(seed)

get_random_numbers cached

get_random_numbers(seed: int) -> NDArray[np.float64]

Summary

Generates an array of random numbers with a specified seed for reproducibility.

Details

Generates a 1D array of 1000 random floating-point numbers distributed uniformly in the half-open interval \([0.0, 1.0)\).

Parameters:

Name	Type	Description	Default
seed	int	The seed value for the random number generator.	required

Returns:

Type	Description
NDArray[float64]	An array of random numbers with shape (1000,).

Examples

>>> from ts_stat_tests.utils.data import get_random_numbers
>>> data = get_random_numbers(42)

>>> print(data.shape)
(1000,)
>>> print(type(data))
<class 'numpy.ndarray'>
>>> print(data.dtype)
float64
>>> print(data[:5])
[0.77395605 0.43887844 0.85859792 0.69736803 0.09417735]

References

1. NumPy Random Generator

Source code in src/ts_stat_tests/utils/data.py

@lru_cache
@typechecked
def get_random_numbers(seed: int) -> NDArray[np.float64]:
    r"""
    !!! note "Summary"
        Generates an array of random numbers with a specified seed for reproducibility.

    ???+ abstract "Details"
        Generates a 1D array of 1000 random floating-point numbers distributed uniformly in the half-open interval $[0.0, 1.0)$.

    Params:
        seed (int):
            The seed value for the random number generator.

    Returns:
        (NDArray[np.float64]):
            An array of random numbers with shape (1000,).

    ???+ example "Examples"

        ```pycon {.py .python linenums="1" title="Setup"}
        >>> from ts_stat_tests.utils.data import get_random_numbers
        >>> data = get_random_numbers(42)

        ```

        ```pycon {.py .python linenums="1" title="Example 1: Generating Random Numbers"}
        >>> print(data.shape)
        (1000,)
        >>> print(type(data))
        <class 'numpy.ndarray'>
        >>> print(data.dtype)
        float64
        >>> print(data[:5])
        [0.77395605 0.43887844 0.85859792 0.69736803 0.09417735]

        ```

    ??? question "References"
        1. [NumPy Random Generator](https://numpy.org/doc/stable/reference/random/generator.html)

    """
    rng: RandomGenerator = get_random_generator(seed)
    return rng.random(size=1000)

get_random_numbers_2d cached

get_random_numbers_2d(seed: int) -> NDArray[np.float64]

Summary

Generates a 2D array of random numbers with a specified seed for reproducibility.

Details

Produces a 2D matrix of shape \((4, 3000)\) containing uniform random values.

Parameters:

Name	Type	Description	Default
seed	int	The seed value for the random number generator.	required

Returns:

Type	Description
NDArray[float64]	A 2D array of random numbers with shape (4, 3000).

Examples

>>> from ts_stat_tests.utils.data import get_random_numbers_2d
>>> data = get_random_numbers_2d(42)

>>> print(data.shape)
(4, 3000)
>>> print(type(data))
<class 'numpy.ndarray'>
>>> print(data.dtype)
float64
>>> print(data[:, :5])
[[0.06206311 0.45826204 0.12903006 0.15232671 0.63228281]
 [0.71609997 0.3571156  0.85186786 0.24097716 0.53839349]
 [0.74315144 0.90157433 0.59866347 0.52857443 0.89016256]
 [0.72072839 0.71123776 0.20269503 0.0366554  0.30379952]]

References

1. NumPy Random Generator

Source code in src/ts_stat_tests/utils/data.py

@lru_cache
@typechecked
def get_random_numbers_2d(seed: int) -> NDArray[np.float64]:
    r"""
    !!! note "Summary"
        Generates a 2D array of random numbers with a specified seed for reproducibility.

    ???+ abstract "Details"
        Produces a 2D matrix of shape $(4, 3000)$ containing uniform random values.

    Params:
        seed (int):
            The seed value for the random number generator.

    Returns:
        (NDArray[np.float64]):
            A 2D array of random numbers with shape (4, 3000).

    ???+ example "Examples"

        ```pycon {.py .python linenums="1" title="Setup"}
        >>> from ts_stat_tests.utils.data import get_random_numbers_2d
        >>> data = get_random_numbers_2d(42)

        ```

        ```pycon {.py .python linenums="1" title="Example 1: Generating 2D Random Numbers"}
        >>> print(data.shape)
        (4, 3000)
        >>> print(type(data))
        <class 'numpy.ndarray'>
        >>> print(data.dtype)
        float64
        >>> print(data[:, :5])
        [[0.06206311 0.45826204 0.12903006 0.15232671 0.63228281]
         [0.71609997 0.3571156  0.85186786 0.24097716 0.53839349]
         [0.74315144 0.90157433 0.59866347 0.52857443 0.89016256]
         [0.72072839 0.71123776 0.20269503 0.0366554  0.30379952]]

        ```

    ??? question "References"
        1. [NumPy Random Generator](https://numpy.org/doc/stable/reference/random/generator.html)

    """
    rng: RandomGenerator = get_random_generator(seed)
    return rng.random(size=(4, 3000))

get_sine_wave cached

get_sine_wave() -> NDArray[np.float64]

Summary

Generates a sine wave dataset.

Details

Produces a 1D array of 1000 samples of a sine wave with amplitude 1.0 and period 1000.

Returns:

Type	Description
NDArray[float64]	An array representing a sine wave with shape (3000,).

Examples

>>> from ts_stat_tests.utils.data import get_sine_wave
>>> data = get_sine_wave()

>>> print(data.shape)
(3000,)
>>> print(type(data))
<class 'numpy.ndarray'>
>>> print(data.dtype)
float64
>>> print(data[:5])
[0.         0.06279052 0.12533323 0.18738131 0.24868989]

References

1. NumPy Trigonometric Functions

Source code in src/ts_stat_tests/utils/data.py

@lru_cache
@typechecked
def get_sine_wave() -> NDArray[np.float64]:
    r"""
    !!! note "Summary"
        Generates a sine wave dataset.

    ???+ abstract "Details"
        Produces a 1D array of 1000 samples of a sine wave with amplitude 1.0 and period 1000.

    Returns:
        (NDArray[np.float64]):
            An array representing a sine wave with shape (3000,).

    ???+ example "Examples"

        ```pycon {.py .python linenums="1" title="Setup"}
        >>> from ts_stat_tests.utils.data import get_sine_wave
        >>> data = get_sine_wave()

        ```

        ```pycon {.py .python linenums="1" title="Example 1: Generating a Sine Wave"}
        >>> print(data.shape)
        (3000,)
        >>> print(type(data))
        <class 'numpy.ndarray'>
        >>> print(data.dtype)
        float64
        >>> print(data[:5])
        [0.         0.06279052 0.12533323 0.18738131 0.24868989]

        ```

    ??? question "References"
        1. [NumPy Trigonometric Functions](https://numpy.org/doc/stable/reference/routines.math.html#trigonometric-functions)

    """
    return np.sin(2 * np.pi * 1 * np.arange(3000) / 100)

get_normal_curve cached

get_normal_curve(seed: int) -> NDArray[np.float64]

Summary

Generates a normal distribution curve dataset.

Details

Draws 1000 samples from a standard Gaussian distribution.

Parameters:

Name	Type	Description	Default
seed	int	The seed value for the random number generator.	required

Returns:

Type	Description
NDArray[float64]	An array representing a normal distribution curve with shape (1000,).

Examples

>>> from ts_stat_tests.utils.data import get_normal_curve
>>> data = get_normal_curve(42)

>>> print(data.shape)
(1000,)
>>> print(type(data))
<class 'numpy.ndarray'>
>>> print(data.dtype)
float64
>>> print(data[:5])
[ 0.12993113 -0.75691222 -0.33007356 -1.88579735 -0.37064992]

References

1. NumPy Random Normal

Source code in src/ts_stat_tests/utils/data.py

@lru_cache
@typechecked
def get_normal_curve(seed: int) -> NDArray[np.float64]:
    r"""
    !!! note "Summary"
        Generates a normal distribution curve dataset.

    ???+ abstract "Details"
        Draws 1000 samples from a standard Gaussian distribution.

    Params:
        seed (int):
            The seed value for the random number generator.

    Returns:
        (NDArray[np.float64]):
            An array representing a normal distribution curve with shape (1000,).

    ???+ example "Examples"

        ```pycon {.py .python linenums="1" title="Setup"}
        >>> from ts_stat_tests.utils.data import get_normal_curve
        >>> data = get_normal_curve(42)

        ```

        ```pycon {.py .python linenums="1" title="Example 1: Generating Normal Curve Data"}
        >>> print(data.shape)
        (1000,)
        >>> print(type(data))
        <class 'numpy.ndarray'>
        >>> print(data.dtype)
        float64
        >>> print(data[:5])
        [ 0.12993113 -0.75691222 -0.33007356 -1.88579735 -0.37064992]

        ```

    ??? question "References"
        1. [NumPy Random Normal](https://numpy.org/doc/stable/reference/random/generated/numpy.random.Generator.normal.html)

    """
    rng: RandomGenerator = get_random_generator(seed)
    return rng.normal(loc=0.0, scale=1.0, size=1000)

get_straight_line cached

get_straight_line() -> NDArray[np.float64]

Summary

Generates a straight line dataset.

Details

Returns a sequence of integers from 0 to 999.

Returns:

Type	Description
NDArray[float64]	An array representing a straight line with shape (1000,).

Examples

>>> from ts_stat_tests.utils.data import get_straight_line
>>> data = get_straight_line()

>>> print(data.shape)
(1000,)
>>> print(type(data))
<class 'numpy.ndarray'>
>>> print(data.dtype)
float64
>>> print(data[:5])
[0. 1. 2. 3. 4.]

References

1. NumPy Arange

Source code in src/ts_stat_tests/utils/data.py

@lru_cache
@typechecked
def get_straight_line() -> NDArray[np.float64]:
    r"""
    !!! note "Summary"
        Generates a straight line dataset.

    ???+ abstract "Details"
        Returns a sequence of integers from 0 to 999.

    Returns:
        (NDArray[np.float64]):
            An array representing a straight line with shape (1000,).

    ???+ example "Examples"

        ```pycon {.py .python linenums="1" title="Setup"}
        >>> from ts_stat_tests.utils.data import get_straight_line
        >>> data = get_straight_line()

        ```

        ```pycon {.py .python linenums="1" title="Example 1: Generating Straight Line Data"}
        >>> print(data.shape)
        (1000,)
        >>> print(type(data))
        <class 'numpy.ndarray'>
        >>> print(data.dtype)
        float64
        >>> print(data[:5])
        [0. 1. 2. 3. 4.]

        ```

    ??? question "References"
        1. [NumPy Arange](https://numpy.org/doc/stable/reference/generated/numpy.arange.html)

    """
    return np.arange(1000).astype(np.float64)

get_trend_data cached

get_trend_data() -> NDArray[np.float64]

Summary

Generates trend data.

Details

Generates an array with a linear trend by combining two ramp functions.

Returns:

Type	Description
NDArray[float64]	An array representing trend data with shape (1000,).

Examples

>>> from ts_stat_tests.utils.data import get_trend_data
>>> data = get_trend_data()

>>> print(data.shape)
(1000,)
>>> print(type(data))
<class 'numpy.ndarray'>
>>> print(data.dtype)
float64
>>> print(data[:5])
[0.  1.5 3.  4.5 6. ]

References

1. NumPy Arange

Source code in src/ts_stat_tests/utils/data.py

@lru_cache
@typechecked
def get_trend_data() -> NDArray[np.float64]:
    r"""
    !!! note "Summary"
        Generates trend data.

    ???+ abstract "Details"
        Generates an array with a linear trend by combining two ramp functions.

    Returns:
        (NDArray[np.float64]):
            An array representing trend data with shape (1000,).

    ???+ example "Examples"

        ```pycon {.py .python linenums="1" title="Setup"}
        >>> from ts_stat_tests.utils.data import get_trend_data
        >>> data = get_trend_data()

        ```

        ```pycon {.py .python linenums="1" title="Example 1: Generating Trend Data"}
        >>> print(data.shape)
        (1000,)
        >>> print(type(data))
        <class 'numpy.ndarray'>
        >>> print(data.dtype)
        float64
        >>> print(data[:5])
        [0.  1.5 3.  4.5 6. ]

        ```

    ??? question "References"
        1. [NumPy Arange](https://numpy.org/doc/stable/reference/generated/numpy.arange.html)

    """
    return np.arange(1000) + 0.5 * np.arange(1000)

get_uniform_data cached

get_uniform_data(seed: int) -> NDArray[np.float64]

Summary

Generates uniform random data with a specified seed for reproducibility.

Details

Returns a 1D array of 1000 samples from a uniform distribution.

Parameters:

Name	Type	Description	Default
seed	int	The seed value for the random number generator.	required

Returns:

Type	Description
NDArray[float64]	An array of uniform random data with shape (1000,).

Examples

>>> from ts_stat_tests.utils.data import get_uniform_data
>>> data = get_uniform_data(42)

>>> print(data.shape)
(1000,)
>>> print(type(data))
<class 'numpy.ndarray'>
>>> print(data.dtype)
float64
>>> print(data[:5])
[0.80227457 0.81857128 0.87962986 0.11378193 0.29263938]

References

1. NumPy Random Uniform

Source code in src/ts_stat_tests/utils/data.py

@lru_cache
@typechecked
def get_uniform_data(seed: int) -> NDArray[np.float64]:
    r"""
    !!! note "Summary"
        Generates uniform random data with a specified seed for reproducibility.

    ???+ abstract "Details"
        Returns a 1D array of 1000 samples from a uniform distribution.

    Params:
        seed (int):
            The seed value for the random number generator.

    Returns:
        (NDArray[np.float64]):
            An array of uniform random data with shape (1000,).

    ???+ example "Examples"

        ```pycon {.py .python linenums="1" title="Setup"}
        >>> from ts_stat_tests.utils.data import get_uniform_data
        >>> data = get_uniform_data(42)

        ```

        ```pycon {.py .python linenums="1" title="Example 1: Generating Uniform Data"}
        >>> print(data.shape)
        (1000,)
        >>> print(type(data))
        <class 'numpy.ndarray'>
        >>> print(data.dtype)
        float64
        >>> print(data[:5])
        [0.80227457 0.81857128 0.87962986 0.11378193 0.29263938]

        ```

    ??? question "References"
        1. [NumPy Random Uniform](https://numpy.org/doc/stable/reference/random/generated/numpy.random.Generator.uniform.html)

    """
    rng: RandomGenerator = get_random_generator(seed)
    return rng.uniform(low=0.0, high=1.0, size=1000)

get_noise_data cached

get_noise_data(seed: int) -> NDArray[np.float64]

Summary

Generates fractional Gaussian noise data with a specified seed for reproducibility.

Details

Uses the stochastic library to sample fractional Gaussian noise with a Hurst exponent of 0.5, effectively producing white noise.

Parameters:

Name	Type	Description	Default
seed	int	The seed value for the random number generator.	required

Returns:

Type	Description
NDArray[float64]	An array of fractional Gaussian noise data with shape (1000,).

Examples

>>> from ts_stat_tests.utils.data import get_noise_data
>>> data = get_noise_data(42)

>>> print(data.shape)
(1000,)
>>> print(type(data))
<class 'numpy.ndarray'>
>>> print(data.dtype)
float64
>>> print(data[:5])
[-0.05413957 -0.0007609  -0.00177524  0.00909899 -0.03044404]

References

1. Stochastic Library

Source code in src/ts_stat_tests/utils/data.py

@lru_cache
@typechecked
def get_noise_data(seed: int) -> NDArray[np.float64]:
    r"""
    !!! note "Summary"
        Generates fractional Gaussian noise data with a specified seed for reproducibility.

    ???+ abstract "Details"
        Uses the `stochastic` library to sample fractional Gaussian noise with a Hurst exponent of 0.5, effectively producing white noise.

    Params:
        seed (int):
            The seed value for the random number generator.

    Returns:
        (NDArray[np.float64]):
            An array of fractional Gaussian noise data with shape (1000,).

    ???+ example "Examples"

        ```pycon {.py .python linenums="1" title="Setup"}
        >>> from ts_stat_tests.utils.data import get_noise_data
        >>> data = get_noise_data(42)

        ```

        ```pycon {.py .python linenums="1" title="Example 1: Generating Noise Data"}
        >>> print(data.shape)
        (1000,)
        >>> print(type(data))
        <class 'numpy.ndarray'>
        >>> print(data.dtype)
        float64
        >>> print(data[:5])
        [-0.05413957 -0.0007609  -0.00177524  0.00909899 -0.03044404]

        ```

    ??? question "References"
        1. [Stochastic Library](https://github.com/crflynn/stochastic)

    """
    rng: RandomGenerator = get_random_generator(seed)
    return FractionalGaussianNoise(hurst=0.5, rng=rng).sample(1000)

load_airline cached

load_airline() -> pd.Series

Summary

Loads the classic Airline Passengers dataset as a pandas Series.

Details

The Air Passengers dataset provides monthly totals of a US airline's international passengers from 1949 to 1960. It is a classic dataset for time series analysis, exhibiting both trend and seasonality.

Returns:

Type	Description
Series	The Airline Passengers dataset.

Examples

>>> from ts_stat_tests.utils.data import load_airline
>>> data = load_airline()

>>> print(len(data))
144
>>> print(type(data))
<class 'pandas.core.series.Series'>
>>> print(data.head())
Period
1949-01    112.0
1949-02    118.0
1949-03    132.0
1949-04    129.0
1949-05    121.0
Freq: M, Name: Number of airline passengers, dtype: float64

Credit

Inspiration from: sktime.datasets.load_airline()

References

1. Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.

Source code in src/ts_stat_tests/utils/data.py

@lru_cache
@typechecked
def load_airline() -> pd.Series:
    r"""
    !!! note "Summary"
        Loads the classic Airline Passengers dataset as a pandas Series.

    ???+ abstract "Details"
        The Air Passengers dataset provides monthly totals of a US airline's international passengers from 1949 to 1960. It is a classic dataset for time series analysis, exhibiting both trend and seasonality.

    Returns:
        (pd.Series):
            The Airline Passengers dataset.

    ???+ example "Examples"

        ```pycon {.py .python linenums="1" title="Setup"}
        >>> from ts_stat_tests.utils.data import load_airline
        >>> data = load_airline()

        ```

        ```pycon {.py .python linenums="1" title="Example 1: Loading Airline Data"}
        >>> print(len(data))
        144
        >>> print(type(data))
        <class 'pandas.core.series.Series'>
        >>> print(data.head())
        Period
        1949-01    112.0
        1949-02    118.0
        1949-03    132.0
        1949-04    129.0
        1949-05    121.0
        Freq: M, Name: Number of airline passengers, dtype: float64

        ```

    ??? success "Credit"
        Inspiration from: [`sktime.datasets.load_airline()`](https://www.sktime.net/en/stable/api_reference/generated/sktime.datasets.load_airline.html)

    ??? question "References"
        1. Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.

    """
    data_source = "https://raw.githubusercontent.com/sktime/sktime/main/sktime/datasets/data/Airline/Airline.csv"
    _data = pd.read_csv(data_source, index_col=0, dtype={1: float}).squeeze("columns")
    if not isinstance(_data, pd.Series):
        raise TypeError("Expected a pandas Series from the data source.")
    data: pd.Series = _data
    data.index = pd.PeriodIndex(data.index, freq="M", name="Period")
    data.name = "Number of airline passengers"
    return data

load_macrodata cached

load_macrodata() -> pd.DataFrame

Summary

Loads the classic Macrodata dataset as a pandas DataFrame.

Details

This dataset contains various US macroeconomic time series from 1959Q1 to 2009Q3. It includes variables such as real GDP, consumption, investment, etc.

Returns:

Type	Description
DataFrame	The Macrodata dataset.

Examples

>>> from ts_stat_tests.utils.data import load_macrodata
>>> data = load_macrodata()

>>> print(data.shape)
(203, 14)
>>> print(type(data))
<class 'pandas.core.frame.DataFrame'>
>>> print(data[["year", "quarter", "realgdp"]].head())
        year  quarter   realgdp
Period
1959Q1  1959        1  2710.349
1959Q2  1959        2  2778.801
1959Q3  1959        3  2775.488
1959Q4  1959        4  2785.204
1960Q1  1960        1  2847.699

Credit

Inspiration from: statsmodels.datasets.macrodata.load_pandas()

References

1. R. F. Engle, D. F. Hendry, and J. F. Richard (1983). Exogeneity. Econometrica, 51(2):277–304.

Source code in src/ts_stat_tests/utils/data.py

@lru_cache
@typechecked
def load_macrodata() -> pd.DataFrame:
    r"""
    !!! note "Summary"
        Loads the classic Macrodata dataset as a pandas DataFrame.

    ???+ abstract "Details"
        This dataset contains various US macroeconomic time series from 1959Q1 to 2009Q3. It includes variables such as real GDP, consumption, investment, etc.

    Returns:
        (pd.DataFrame):
            The Macrodata dataset.

    ???+ example "Examples"

        ```pycon {.py .python linenums="1" title="Setup"}
        >>> from ts_stat_tests.utils.data import load_macrodata
        >>> data = load_macrodata()

        ```

        ```pycon {.py .python linenums="1" title="Example 1: Loading Macrodata"}
        >>> print(data.shape)
        (203, 14)
        >>> print(type(data))
        <class 'pandas.core.frame.DataFrame'>
        >>> print(data[["year", "quarter", "realgdp"]].head())
                year  quarter   realgdp
        Period
        1959Q1  1959        1  2710.349
        1959Q2  1959        2  2778.801
        1959Q3  1959        3  2775.488
        1959Q4  1959        4  2785.204
        1960Q1  1960        1  2847.699

        ```

    ??? success "Credit"
        Inspiration from: [`statsmodels.datasets.macrodata.load_pandas()`](https://www.statsmodels.org/stable/datasets/generated/statsmodels.datasets.macrodata.macrodata.load_pandas.html)

    ??? question "References"
        1. R. F. Engle, D. F. Hendry, and J. F. Richard (1983). Exogeneity. Econometrica, 51(2):277–304.

    """
    data_source = (
        "https://raw.githubusercontent.com/statsmodels/statsmodels/main/statsmodels/datasets/macrodata/macrodata.csv"
    )
    data: pd.DataFrame = pd.read_csv(
        data_source,
        index_col=None,
        dtype={
            "year": int,
            "quarter": int,
        },
    )
    data.index = pd.PeriodIndex(
        data=data.year.astype(str) + "Q" + data.quarter.astype(str),
        freq="Q",
        name="Period",
    )
    return data

data_airline module-attribute

data_airline: Series = load_airline()

data_macrodata module-attribute

data_macrodata: DataFrame = load_macrodata()

data_random module-attribute

data_random: NDArray[float64] = get_random_numbers(SEED)

data_random_2d module-attribute

data_random_2d: NDArray[float64] = get_random_numbers_2d(
    SEED
)

data_sine module-attribute

data_sine: NDArray[float64] = get_sine_wave()

data_normal module-attribute

data_normal: NDArray[float64] = get_normal_curve(SEED)

data_line module-attribute

data_line: NDArray[float64] = get_straight_line()

data_trend module-attribute

data_trend: NDArray[float64] = get_trend_data()

data_noise module-attribute

data_noise: NDArray[float64] = get_noise_data(SEED)