diff --git a/tslearn/metrics/sbd.py b/tslearn/metrics/sbd.py
new file mode 100644
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+++ b/tslearn/metrics/sbd.py
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+import numpy as np
+
+from numpy.linalg import norm
+from numpy.fft import fft, ifft
+
+
+def sbd(s1, s2):
+    """Compute the Shape-based distance (SBD) measure between two time series and return the distance between the given
+    time series and the shift of s2 that maximizes
+
+    The computation of SBD is based on cross-correlation. Cross-correlation is a statistical
+    measure to determine the similarity of two sequences ~s1 and ~s2, even if they are not properly aligned.
+    To achieve shift-invariance, cross-correlation keeps ~s1 static and slides ~s2 over ~s1 to compute
+    their inner product for each shift s of ~s2. SBD is then calculated as 1 - the coefficient normalized
+    cross-correlation of the optimal shift of s2, where the optimal shift of s2 is the one that maximizes the
+    coefficient normalized cross-correlation.
+
+    SBD takes values between 0 to 2, with 0 indicating perfect similarity for time-series sequences.
+    SBD was originally presented in [1]_.
+    Parameters
+    ----------
+    s1
+        A time series.
+    s2
+        Another time series.
+
+    Returns
+    -------
+    float
+        The Shape-based distance (SBD) measure between s1 and s2
+    numpy.ndarray
+        The shift of s2
+
+    Examples
+    --------
+    >>> sbd_dist, s2_shift = sbd([1, 2, 3], [1, 2, 3])
+    >>> sbd_dist
+    0.0
+    >>> type(s2_shift)
+    <class 'numpy.ndarray'>
+    >>> s2_shift
+    array([1, 2, 3])
+
+    References
+    ----------
+    .. [1] John Paparrizos and Luis Gravano. 2015. K-Shape: Efficient and Accurate Clustering of Time Series. In
+    Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (SIGMOD '15).
+    """
+    # Get all possible shifts and their coefficient normalized cross-correlation
+    ncc = _ncc_c(s1, s2)
+    # Choose the shift with the highest coefficient normalized cross-correlation
+    idx = ncc.argmax()
+    # Calculate SBD according to Equation 9
+    dist = 1 - ncc[idx]
+    # Pad the chosen s2_shift with zeros
+    s2_shift = _roll_zeropad(s2, (idx + 1) - max(len(s1), len(s2)))
+
+    return dist, s2_shift
+
+
+def _ncc_c(s1, s2):
+    den = np.array(norm(s1) * norm(s2))
+    den[den == 0] = np.Inf
+
+    s1_len = len(s1)
+    # Performance optimization: Calculate the next power-of-two, fft will then pad with zeros to reach fft_size
+    fft_size = 1 << (2 * s1_len - 1).bit_length()
+    # Equation 12
+    cc = ifft(fft(s1, fft_size) * np.conj(fft(s2, fft_size)))
+    cc = np.concatenate((cc[-(s1_len - 1):], cc[:s1_len]))
+    return np.real(cc) / den
+
+
+def _roll_zeropad(a, shift, axis=None):
+    a = np.asanyarray(a)
+    if shift == 0:
+        return a
+    if axis is None:
+        n = a.size
+        reshape = True
+    else:
+        n = a.shape[axis]
+        reshape = False
+    if np.abs(shift) > n:
+        res = np.zeros_like(a)
+    elif shift < 0:
+        shift += n
+        zeros = np.zeros_like(a.take(np.arange(n - shift), axis))
+        res = np.concatenate((a.take(np.arange(n - shift, n), axis), zeros), axis)
+    else:
+        zeros = np.zeros_like(a.take(np.arange(n - shift, n), axis))
+        res = np.concatenate((zeros, a.take(np.arange(n - shift), axis)), axis)
+    if reshape:
+        return res.reshape(a.shape)
+    else:
+        return res