kats/models/nowcasting/feature_extraction.py [219:251]:
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        nowcasting_features = [np.nan for _ in range(7)]

        # ROC: indicating return comparing to step n back.
        if extra_args is not None and extra_args.get("nowcast_roc", default_status):
            M = x[(window - 1) :] - x[: -(window - 1)]
            N = x[: -(window - 1)]
            arr = M / N
            nowcasting_features[0] = np.nan_to_num(
                arr, nan=0.0, posinf=0.0, neginf=0.0
            ).mean()

        # MOM: indicating momentum: difference of current value and n steps back.
        if extra_args is not None and extra_args.get("nowcast_mom", default_status):
            M = x[window:] - x[:-window]
            nowcasting_features[1] = np.nan_to_num(
                M, nan=0.0, posinf=0.0, neginf=0.0
            ).mean()

        # MA: indicating moving average in the past n steps.
        if extra_args is not None and extra_args.get("nowcast_ma", default_status):
            ret = np.cumsum(x, dtype=float)
            ret[window:] = ret[window:] - ret[:-window]
            ma = ret[window - 1 :] / window
            nowcasting_features[2] = np.nan_to_num(
                ma, nan=0.0, posinf=0.0, neginf=0.0
            ).mean()

        # LAG: indicating lagged value at the past n steps.
        if extra_args is not None and extra_args.get("nowcast_lag", default_status):
            N = x[:-window]
            nowcasting_features[3] = np.nan_to_num(
                N, nan=0.0, posinf=0.0, neginf=0.0
            ).mean()
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kats/tsfeatures/tsfeatures.py [1733:1765]:
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        nowcasting_features = [np.nan for _ in range(7)]

        # ROC: indicating return comparing to step n back.
        if extra_args is not None and extra_args.get("nowcast_roc", default_status):
            M = x[(window - 1) :] - x[: -(window - 1)]
            N = x[: -(window - 1)]
            arr = M / N
            nowcasting_features[0] = np.nan_to_num(
                arr, nan=0.0, posinf=0.0, neginf=0.0
            ).mean()

        # MOM: indicating momentum: difference of current value and n steps back.
        if extra_args is not None and extra_args.get("nowcast_mom", default_status):
            M = x[window:] - x[:-window]
            nowcasting_features[1] = np.nan_to_num(
                M, nan=0.0, posinf=0.0, neginf=0.0
            ).mean()

        # MA: indicating moving average in the past n steps.
        if extra_args is not None and extra_args.get("nowcast_ma", default_status):
            ret = np.cumsum(x, dtype=float)
            ret[window:] = ret[window:] - ret[:-window]
            ma = ret[window - 1 :] / window
            nowcasting_features[2] = np.nan_to_num(
                ma, nan=0.0, posinf=0.0, neginf=0.0
            ).mean()

        # LAG: indicating lagged value at the past n steps.
        if extra_args is not None and extra_args.get("nowcast_lag", default_status):
            N = x[:-window]
            nowcasting_features[3] = np.nan_to_num(
                N, nan=0.0, posinf=0.0, neginf=0.0
            ).mean()
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