diff --git a/sdp/challenger_sdp.py b/sdp/challenger_sdp.py
index ce60e8d..e987c6f 100644
--- a/sdp/challenger_sdp.py
+++ b/sdp/challenger_sdp.py
@@ -1,4 +1,35 @@
 import numpy as np
+from scipy.fft import dct
+
+
+def _sliding_dot_product(Q, T):
+    # MASS_V4 in https://www.cs.unm.edu/~mueen/FastestSimilaritySearch.html
+    m = Q.shape[0]
+    n = T.shape[0]
+    p1 = (n - m + 1) // 2
+    p2 = (m + 1) // 2
+    N = p1 + p2 + n  # The length of Q_padded and T_padded
+
+    # Pad Q and T
+    Q_padded = np.empty(N, dtype=np.float64)
+    Q_padded[:p1] = 0
+    Q_padded[p1 : p1 + m] = Q
+    Q_padded[p1 + m :] = 0
+
+    T_padded = np.empty(N, dtype=np.float64)
+    T_padded[: p1 + p2] = 0
+    T_padded[p1 + p2 :] = T
+
+    # Use DCT to compute the sliding dot product
+    QT_dct = np.empty(N + 1, dtype=np.float64)
+    QT_dct[:N] = dct(Q_padded, type=2, norm="ortho")
+    np.multiply(QT_dct[:N], dct(T_padded, type=2, norm="ortho"), out=QT_dct[:N])
+    QT_dct[0] *= np.sqrt(2)
+    QT_dct[N] = 0
+
+    QT_dct[:] = dct(QT_dct, type=1, norm="ortho")
+
+    return np.sqrt(2 * N) * QT_dct[p2 : p2 + (n - m + 1)]
 
 
 def setup(Q, T):
@@ -6,9 +37,4 @@ def setup(Q, T):
 
 
 def sliding_dot_product(Q, T):
-    m = len(Q)
-    l = T.shape[0] - m + 1
-    out = np.empty(l)
-    for i in range(l):
-        out[i] = np.dot(Q, T[i : i + m])
-    return out
+    return _sliding_dot_product(Q, T)