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for i in range(n_feats):
        cur_position = 0
        num_short_features = len(short_features[i])
        mid_feature_names[i] = short_feature_names[i] + "_" + "mean"
        mid_feature_names[i + n_feats] = short_feature_names[i] + "_" + "std"
        while cur_position < num_short_features:
            end = cur_position + mid_window_ratio
            if end > num_short_features:
                end = num_short_features
            cur_st_feats = short_features[i][cur_position:end]
            mid_features[i].append(np.mean(cur_st_feats))
            mid_features[i + n_feats].append(np.std(cur_st_feats))
            cur_position += mt_step_ratio
    mid_features = np.array(mid_features)
    mid_features = np.nan_to_num(mid_features)
    return mid_features, short_features, mid_feature_names