Automatic stationary conversion

def make_stationary(data: pd.Series, alpha: float = 0.05, max_diff_order: int = 10) -> dict:
    # Test to see if the time series is already stationary
    if adfuller(data)[1] < alpha:
        return {
            'differencing_order': 0,
            'time_series': np.array(data)
        }
    
    # A list to store P-Values
    p_values = []
    
    # Test for differencing orders from 1 to max_diff_order (included)
    for i in range(1, max_diff_order + 1):
        # Perform ADF test
        result = adfuller(data.diff(i).dropna())
        # Append P-value
        p_values.append((i, result[1]))
        
    # Keep only those where P-value is lower than significance level
    significant = [p for p in p_values if p[1] < alpha]
    # Sort by the differencing order
    significant = sorted(significant, key=lambda x: x[0])
    
    # Get the differencing order
    diff_order = significant[0][0]
    
    # Make the time series stationary
    stationary_series = data.diff(diff_order).dropna()
    
    return {
        'differencing_order': diff_order,
        'time_series': np.array(stationary_series)
    }

Source: gist.githubusercontent.com

Similar codes

1 vote134 views1 answer
'set' object is not reversible
Python
0 vote141 views1 answer
Python die
Python
1 vote131 views1 answer
Gimp python
Python
-1 vote142 views2 answers
Python datetime to string iso 8601
Python
13 votes145 views5 answers
Converting numpy array to dataframe
Python
0 vote136 views1 answer
Python get item from list by index
Python
69 votes125 views6 answers
Python shuffle array
Python
0 vote122 views1 answer
Typehinting with when parameter has default
Python
0 vote119 views1 answer
Mad python
Python
1 vote120 views1 answer
Python print string name in pattern
Python