def METHOD_NAME(q, t, q_len, t_len):
    """
    Compute the sliding dot products between a query and a time series.
    Parameters
    ----------
        q: numpy.array
            Query.
        t: numpy.array
            Time series.
        q_len: int
            Length of the query.
        t_len: int
            Length of the time series.
    Output
    ------
        dot_prod: numpy.array
                    Sliding dot products between q and t.
    """
    # Reversing query and padding both query and time series
    t_padded = np.pad(t, (0, t_len))
    q_reversed = np.flipud(q)
    q_reversed_padded = np.pad(q_reversed, (0, 2 * t_len - q_len))
    # Applying FFT to both query and time series
    t_fft = np.fft.fft(t_padded)
    q_fft = np.fft.fft(q_reversed_padded)
    # Applying inverse FFT to obtain the convolution of the time series by
    # the query
    element_wise_mult = np.multiply(t_fft, q_fft)
    inverse_fft = np.fft.ifft(element_wise_mult)
    # Returns only the valid dot products from inverse_fft
    dot_prod = inverse_fft[q_len - 1 : t_len].real
    return dot_prod