def rolling_std(data, lookback_mean=1, lookback_std=10, k=1):
    """method to get anomalies as dates (DatetimeIndex) (dtype=datetime64[ns])
    - rolling mean mu_hat(n)
    - rolling std sigma_hat(n)
    - data points xn
    - anomalies definition: xn - mu_hat(n-1) > k*std_hat(n-1)

    - maximum ten anomalies (10 'biggest' anomalies)

    :argument data: dataframe with one column (interest) and pandas dates as index
    :argument k: float

    :returns anomalies as DatetimeIndex sorted(dtype=datetime64[ns])
    """
    mean = data.rolling(lookback_mean).mean().shift(1)
    std = data.rolling(lookback_std).std().shift(1)
    tmp = data - mean - k * std
    return tmp[tmp.iloc[:, (0)] > 0].sort_values(by=tmp.columns[0],
        ascending=False)[0:10].index.sort_values()
