def prepare_features()

in afa/core.py [0:0]

• 26 lines of code
• 11 McCabe index (conditional complexity)

    def prepare_features(self, x):
        if self.d > 0:
            x = difference(x, self.d)
                    
        ar_features = None
        ma_features = None
        
        # Determine the features and the epsilon terms for the MA process
        if self.q > 0:
            if self.ar is None:
                self.ar = ARIMA(0, 0, self.p)
                self.ar.fit_predict(x)
            eps = self.ar.resid
            eps[0] = 0
            
            # prepend with zeros as there are no residuals_t-k in the first X_t
            ma_features, _ = self._lag_view(np.r_[np.zeros(self.q), eps], self.q)
            
        # Determine the features for the AR process
        if self.p > 0:
            # prepend with zeros as there are no X_t-k in the first X_t
            ar_features = self._lag_view(np.r_[np.zeros(self.p), x], self.p)[0]
                                
        if ar_features is not None and ma_features is not None:
            n = min(len(ar_features), len(ma_features)) 
            ar_features = ar_features[:n]
            ma_features = ma_features[:n]
            features = np.hstack((ar_features, ma_features))
        elif ma_features is not None: 
            n = len(ma_features)
            features = ma_features[:n]
        else:
            n = len(ar_features)
            features = ar_features[:n]
        
        return features, x[:n]