data_describe.core.time
==============================

.. py:module:: data_describe.core.time





.. autoapisummary::

   data_describe.core.time.plot_time_series
   data_describe.core.time.stationarity_test
   data_describe.core.time.plot_autocorrelation
   data_describe.core.time.adf_test
   data_describe.core.time.kpss_test
   data_describe.core.time.figure_layout


.. function:: plot_time_series(df, col, decompose=False, model='additive', compute_backend=None, viz_backend=None, **kwargs)

   Plots time series given a dataframe with datetime index. Statistics are computed using the statsmodels API.

   :param df: The dataframe with datetime index
   :param col: Column of interest. Column datatype must be numerical
   :type col: str or [str]
   :param decompose: Set as True to decompose the timeseries with moving average. Defaults to False.
   :param model: Specify seasonal component when decompose is True. Defaults to "additive".
   :param compute_backend: Select computing backend. Defaults to None (pandas).
   :param viz_backend: Select visualization backend. Defaults to None (seaborn).
   :param \*\*kwargs: Keyword arguments

   :raises ValueError: Invalid input data type.
   :raises ValueError: ```col``` not a list or string.

   :returns: The visualization


.. function:: stationarity_test(df, col, test='dickey-fuller', regression='c', compute_backend=None, **kwargs)

   Perform stationarity tests to see if mean and variance are changing over time.

   Backend uses statsmodel's statsmodels.tsa.stattools.adfuller or statsmodels.tsa.stattools.kpss

   :param df: The dataframe. Must contain a datetime index
   :param col: The feature of interest
   :param test: Choice of stationarity test. "kpss" or "dickey-fuller". Defaults to "dickey-fuller".
   :param regression: Constant and trend order to include in regression. Choose between 'c','ct','ctt', and 'nc'. Defaults to 'c'
   :param compute_backend: Select computing backend. Defaults to None (pandas).
   :param \*\*kwargs: Keyword arguments

   :raises ValueError: Invalid input data type.
   :raises ValueError: `col` not found in dataframe.

   :returns: Pandas dataframe containing the statistics


.. function:: plot_autocorrelation(df, col, plot_type='acf', n_lags=40, fft=False, compute_backend=None, viz_backend=None, **kwargs)

   Correlation estimate using partial autocorrelation or autocorrelation.

   Statistics are computed using the statsmodels API.

   :param df: The dataframe with datetime index
   :param col: The feature of interest
   :param plot_type: Choose between 'acf' or 'pacf. Defaults to "pacf".
   :param n_lags: Number of lags to return autocorrelation for. Defaults to 40.
   :param fft: If True, computes ACF via fourier fast transform (FFT). Defaults to False.
   :param compute_backend: Select computing backend. Defaults to None (pandas).
   :param viz_backend: Select visualization backend. Defaults to None (seaborn).
   :param \*\*kwargs: Keyword arguments

   :raises ValueError: Invalid input data type.
   :raises ValueError: `col` not found in dataframe.

   :returns: The visualization


.. function:: adf_test(timeseries, autolag: str = 'AIC', regression: str = 'c', **kwargs)

   Compute the Augmented Dickey-Fuller (ADF) test for stationarity.

   Backend uses statsmodels.tsa.stattools.adfuller

   :param timeseries: The timeseries
   :param autolag: Method to use when determining the number of lags. Defaults to 'AIC'. Choose between 'AIC', 'BIC', 't-stat', and None
   :param regression: Constant and trend order to include in regression. Choose between 'c','ct','ctt', and 'nc'
   :param \*\*kwargs: Keyword arguments for adfuller

   :returns: Pandas dataframe containing the statistics


.. function:: kpss_test(timeseries, regression: str = 'c', nlags: Optional[int] = None, **kwargs)

   Compute the Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test for stationarity.

   Backend uses statsmodels.tsa.stattools.kpss

   :param timeseries: The timeseries
   :param regression: The null hypothesis for the KPSS test.
                      'c' : The data is stationary around a constant (default).
                      'ct' : The data is stationary around a trend.
   :param nlags: Indicates the number of lags to be used. Defaults to None.
   :param \*\*kwargs: Keyword arguments for kpss

   :returns: Pandas dataframe containing the statistics


.. function:: figure_layout(title='Time Series', xlabel='Date', ylabel='Variable')

   Generates the figure layout.

   :param title: Title of the plot. Defaults to "Time Series".
   :param xlabel: x-axis label. Defaults to "Date".
   :param ylabel: y-axis label. Defaults to "Variable".

   :returns: The plotly layout