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diff --git a/src/python/gudhi/point_cloud/timedelay.py b/src/python/gudhi/point_cloud/timedelay.py
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+# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT.
+# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details.
+# Author(s):       Martin Royer, Yuichi Ike, Masatoshi Takenouchi
+#
+# Copyright (C) 2020 Inria, Copyright (C) 2020 Fujitsu Laboratories Ltd.
+# Modification(s):
+#   - YYYY/MM Author: Description of the modification
+
+import numpy as np
+
+
+class TimeDelayEmbedding:
+    """Point cloud transformation class. Embeds time-series data in the R^d according to
+    `Takens' Embedding Theorem <https://en.wikipedia.org/wiki/Takens%27s_theorem>`_ and obtains the
+    coordinates of each point.
+
+    Parameters
+    ----------
+    dim : int, optional (default=3)
+        `d` of R^d to be embedded.
+    delay : int, optional (default=1)
+        Time-Delay embedding.
+    skip : int, optional (default=1)
+        How often to skip embedded points.
+
+    Example
+    -------
+
+    Given delay=3 and skip=2, a point cloud which is obtained by embedding
+    a scalar time-series into R^3 is as follows::
+
+        time-series = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+        point cloud = [[1, 4, 7],
+                       [3, 6, 9]]
+
+    Given delay=1 and skip=1, a point cloud which is obtained by embedding
+    a 2D vector time-series data into R^4 is as follows::
+
+        time-series = [[0, 1], [2, 3], [4, 5], [6, 7], [8, 9]]
+        point cloud = [[0, 1, 2, 3],
+                       [2, 3, 4, 5],
+                       [4, 5, 6, 7],
+                       [6, 7, 8, 9]]
+    """
+
+    def __init__(self, dim=3, delay=1, skip=1):
+        self._dim = dim
+        self._delay = delay
+        self._skip = skip
+
+    def __call__(self, ts):
+        """Transform method for single time-series data.
+
+        Parameters
+        ----------
+        ts : Iterable[float] or Iterable[Iterable[float]]
+            A single time-series data, with scalar or vector values.
+
+        Returns
+        -------
+        point cloud : n x dim numpy arrays
+            Makes point cloud from a single time-series data.
+        """
+        return self._transform(np.array(ts))
+
+    def fit(self, ts, y=None):
+        return self
+    
+    def _transform(self, ts):
+        """Guts of transform method."""
+        if ts.ndim == 1:
+            repeat = self._dim
+        else:
+            assert self._dim % ts.shape[1] == 0
+            repeat = self._dim // ts.shape[1]
+        end = len(ts) - self._delay * (repeat - 1)
+        short = np.arange(0, end, self._skip)
+        vertical = np.arange(0, repeat * self._delay, self._delay)
+        return ts[np.add.outer(short, vertical)].reshape(len(short), -1)
+
+    def transform(self, ts):
+        """Transform method for multiple time-series data.
+
+        Parameters
+        ----------
+        ts : Iterable[Iterable[float]] or Iterable[Iterable[Iterable[float]]]
+            Multiple time-series data, with scalar or vector values.
+
+        Returns
+        -------
+        point clouds : list of n x dim numpy arrays
+            Makes point cloud from each time-series data.
+        """
+        return [self._transform(np.array(s)) for s in ts]