Knk00

from scipy.stats import kruskal

def seasonality_test(series):
        seasoanl = False
        idx = np.arange(len(series.index)) % 12
        H_statistic, p_value = kruskal(series, idx)
        if p_value <= 0.05:
            seasonal = True
        return seasonal

Knk00

   '''Helper Funciton to check if seasonality is present'''
   def seasonality_test(self, series):
        self.__seasonal__ = False
        idx = np.arange(len(series.index)) % 12
        H_statistic, p_value = kruskal(series, idx)
        if p_value <= 0.05:
            self.__seasonal__ = True
        return seasonal
    
     '''Helper Function to check if trend is present'''

Knk00

def ADF_test (self, series):    
        
        adf = adfuller(series)    
        print('ADF test')
        print("\tADF Statistic: {}\n".format (adf[0])) 
        print("\tp-value: {}\n".format (adf[1]))  #Since the p-value is far from 0.05 it not stationary
        print ("\tCritical values : \n")
        for key, value in adf[4].items():
            print("\t\t{} : {}\n".format(key, value))

Knk00

class model:
    
    '''Attributes of the class are the same as the ones the describe any time series : Trend, Seasonality; additionally
    Root-Mean-Squared Error and Model type (additive/multiplicative) are also taken into consideration'''
    
    def __init__(self, series, rmse = None, model = None):
        
        if rmse == None:
            self.rmse : float = float('inf')
        else: