def _ts_slice(self, Xt, y):
        ''' takes time series data, and splits each series into temporal folds '''
        Ns = len(Xt)
        Xt_new = []
        for i in range(self.n_splits):
            for j in range(Ns):
                Njs = int(len(Xt[j]) / self.n_splits)
                Xt_new.append(Xt[j][(Njs * i):(Njs * (i + 1))])
        Xt_new = np.array(Xt_new)

        if len(np.atleast_1d(y[0])) == len(Xt[0]):
            # y is a time series
            y_new = []
            for i in range(self.n_splits):
                for j in range(Ns):
                    Njs = int(len(y[j]) / self.n_splits)
                    y_new.append(y[j][(Njs * i):(Njs * (i + 1))])
            y_new = np.array(y_new)
        else:
            # y is contextual to each series
            y_new = np.concatenate([y for i in range(self.n_splits)])

        return Xt_new, y_new
takes time series data, and splits each series into temporal folds