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

3 votes136 views3 answers
List sort by key python
Python
4 votes134 views3 answers
Distance formula in python
Python
0 vote144 views1 answer
Download folder collab
Python
0 vote150 views1 answer
Python get duration of wav file
Python
7 votes202 views2 answers
Log base in python
Python
0 vote135 views1 answer
Python version of settimout
Python
27 votes149 views3 answers
Pandas replce none with nan
Python
0 vote151 views1 answer
Yield value from csv file python
Python
0 vote149 views1 answer
Python __repr__ meaning
Python
1 vote133 views1 answer
Flask socketio send
Python