ABOUT

    This module contains functions for time series analysis, including visualizations, statistical tests, 
    and model evaluation metrics.
    
    Imports:
    --------
    - pandas: for data manipulation and analysis.
    - matplotlib.pyplot: for plotting.
    - seaborn: for advanced plotting.
    - scipy.signal: for signal processing functions, including periodograms.
    - statsmodels: for statistical modeling, including time series analysis.
    - sklearn.metrics: for model evaluation metrics.
    
    Functions:
    ----------
    1. show_periodogram(data, detrend='linear', ax=None, fs=365, color='brown'):
        Displays the periodogram of the given time series data.
    
    2. show_seasonal(df, period, freq, ax=None, title=None, x_label=None, y_label=None):
        Creates seasonal plots for visualizing seasonal patterns in time series data.
    
    3. show_lags(data, n_lags=10, title='Lag Plots'):
        Generates lag plots to visualize the autocorrelation of a time series.
    
    4. adf_test(df):
        Performs the Augmented Dickey-Fuller test to check for stationarity in the time series.
    
    5. show_correlogram(data, lags=6, ACF=True, PACF=True):
        Plots the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) of the time series.
    
    6. evaluate(models_forecast, test_set, view='results'):
        Evaluates multiple forecasting models and displays their performance metrics.
    7. split_data(df, train_proportion = 0.8):
        Splits a DataFrame into training and testing sets.

HOW TO IMPORT THESE FUNCTIONS?

    All Functions: from time_series.ts_tools import *
    Some Functions: from time_series.ts_tools import show_seasonal, show_periodogram