diff --git a/include/algorithms/public/DTW.hpp b/include/algorithms/public/DTW.hpp
new file mode 100644
index 000000000..f929a5faf
--- /dev/null
+++ b/include/algorithms/public/DTW.hpp
@@ -0,0 +1,238 @@
+/*
+Part of the Fluid Corpus Manipulation Project (http://www.flucoma.org/)
+Copyright University of Huddersfield.
+Licensed under the BSD-3 License.
+See license.md file in the project root for full license information.
+This project has received funding from the European Research Council (ERC)
+under the European Union’s Horizon 2020 research and innovation programme
+(grant agreement No 725899).
+*/
+
+#pragma once
+
+#include "../util/FluidEigenMappings.hpp"
+#include "../../data/FluidDataSet.hpp"
+#include "../../data/FluidIndex.hpp"
+#include "../../data/FluidMemory.hpp"
+#include "../../data/FluidTensor.hpp"
+#include "../../data/TensorTypes.hpp"
+#include <Eigen/Core>
+#include <cstddef>
+#include <iterator>
+#include <random>
+
+namespace fluid {
+namespace algorithm {
+
+
+enum class DTWConstraint { kUnconstrained, kIkatura, kSakoeChiba };
+
+// debt of gratitude to the wonderful article on
+// https://rtavenar.github.io/blog/dtw.html a better explanation of DTW than any
+// other algorithm explanation I've seen
+
+class DTW
+{
+  struct Constraint;
+
+public:
+  explicit DTW() = default;
+  ~DTW() = default;
+
+  void init() {}
+  void clear() {}
+
+  index           size() const { return mPNorm; }
+  constexpr index dims() const { return 0; }
+  constexpr index initialized() const { return true; }
+
+  double process(InputRealMatrixView x1, InputRealMatrixView x2,
+                 DTWConstraint constr = DTWConstraint::kUnconstrained,
+                 index         param = 2,
+                 Allocator&    alloc = FluidDefaultAllocator()) const
+  {
+    ScopedEigenMap<Eigen::VectorXd> x1r(x1.cols(), alloc),
+        x2r(x2.cols(), alloc);
+    Constraint constraint(constr, x1.rows(), x2.rows(), param);
+
+    mDistanceMetrics.resize(x1.rows(), x2.rows());
+    mDistanceMetrics.fill(std::numeric_limits<double>::max());
+
+    constraint.iterate([&, this](index r, index c) {
+      x1r = _impl::asEigen<Eigen::Matrix>(x1.row(r));
+      x2r = _impl::asEigen<Eigen::Matrix>(x2.row(c));
+
+      mDistanceMetrics(r, c) = differencePNormToTheP(x1r, x2r);
+
+      if (r > 0 || c > 0)
+      {
+        double minimum = std::numeric_limits<double>::max();
+
+        if (r > 0) minimum = std::min(minimum, mDistanceMetrics(r - 1, c));
+        if (c > 0) minimum = std::min(minimum, mDistanceMetrics(r, c - 1));
+        if (r > 0 && c > 0)
+          minimum = std::min(minimum, mDistanceMetrics(r - 1, c - 1));
+
+        mDistanceMetrics(r, c) += minimum;
+      }
+    });
+
+    return std::pow(mDistanceMetrics(x1.rows() - 1, x2.rows() - 1),
+                    1.0 / mPNorm);
+  }
+
+private:
+  mutable RealMatrix mDistanceMetrics;
+  index              mPNorm{2};
+
+  // P-Norm of the difference vector
+  // Lp{vec} = (|vec[0]|^p + |vec[1]|^p + ... + |vec[n-1]|^p + |vec[n]|^p)^(1/p)
+  // i.e., the 2-norm of a vector is the euclidian distance from the origin
+  //       the 1-norm is the sum of the absolute value of the elements
+  // To the power P since we'll be summing multiple Norms together and they
+  // can combine into a single norm if you calculate the norm of multiple norms
+  // (normception)
+  inline double
+  differencePNormToTheP(const Eigen::Ref<const Eigen::VectorXd>& v1,
+                        const Eigen::Ref<const Eigen::VectorXd>& v2) const
+  {
+    // assert(v1.size() == v2.size());
+    return (v1.array() - v2.array()).abs().pow(mPNorm).sum();
+  }
+
+  // fun little fold operation to do a variadic minimum
+  template <typename... Args>
+  inline static auto min(Args&&... args)
+  {
+    auto m = (args, ...);
+    return ((m = std::min(m, args)), ...);
+  }
+
+  // filter for minimum chaining, if cond evaluates to false then the value
+  // isn't used (never will be the minimum if its the numeric maximum)
+  template <typename T>
+  inline static T useIf(bool cond, T val)
+  {
+    return cond ? val : std::numeric_limits<T>::max();
+  }
+
+  struct Constraint
+  {
+    Constraint(DTWConstraint c, index rows, index cols, float param)
+        : mType{c}, mRows{rows}, mCols{cols}, mParam{param}
+    {
+      // ifn't gradient more than digonal set it to be the diagonal
+      // (sakoe-chiba with radius 0)
+      if (c == DTWConstraint::kIkatura)
+      {
+        float big = std::max(mRows, mCols), smol = std::min(mRows, mCols);
+
+        if (mParam <= big / smol)
+        {
+          mType = DTWConstraint::kSakoeChiba;
+          mParam = 0;
+        }
+      }
+    };
+
+    void iterate(std::function<void(index, index)> f)
+    {
+      index first, last;
+
+      for (index r = 0; r < mRows; ++r)
+      {
+        first = firstCol(r);
+        last = lastCol(r);
+
+        for (index c = first; c <= last; ++c) f(r, c);
+      }
+    };
+
+  private:
+    DTWConstraint mType;
+    index         mRows, mCols;
+    float         mParam; // mParam is either radius (SC) or gradient (Ik)
+
+    inline static index rasterLineMinY(index x1, index y1, float dydx, index x)
+    {
+      return std::round(y1 + (x - x1) * dydx);
+    }
+
+    inline static index rasterLineMinY(index x1, index y1, index x2, index y2,
+                                       index x)
+    {
+      float dy = y2 - y1, dx = x2 - x1;
+      return rasterLineMinY(x1, y1, dy / dx, x);
+    }
+
+    inline static index rasterLineMaxY(index x1, index y1, float dydx, index x)
+    {
+      if (dydx > 1)
+        return rasterLineMinY(x1, y1, dydx, x + 1) - 1;
+      else
+        return rasterLineMinY(x1, y1, dydx, x);
+    }
+
+    inline static index rasterLineMaxY(index x1, index y1, index x2, index y2,
+                                       index x)
+    {
+      float dy = y2 - y1, dx = x2 - x1;
+      return rasterLineMaxY(x1, y1, dy / dx, x);
+    }
+
+    index firstCol(index row)
+    {
+      switch (mType)
+      {
+      case DTWConstraint::kUnconstrained: return 0;
+
+      case DTWConstraint::kIkatura: {
+        index colNorm = rasterLineMinY(mRows - 1, mCols - 1, mParam, row);
+        index colInv = rasterLineMinY(0, 0, 1 / mParam, row);
+
+        index col = std::max(colNorm, colInv);
+
+        return col < 0 ? 0 : col > mCols - 1 ? mCols - 1 : col;
+      }
+
+      case DTWConstraint::kSakoeChiba: {
+        index col = rasterLineMinY(mParam, -mParam, mRows - 1 + mParam,
+                                   mCols - 1 - mParam, row);
+
+        return col < 0 ? 0 : col > mCols - 1 ? mCols - 1 : col;
+      }
+      }
+
+      return 0;
+    };
+
+    index lastCol(index row)
+    {
+      switch (mType)
+      {
+      case DTWConstraint::kUnconstrained: return mCols - 1;
+
+      case DTWConstraint::kIkatura: {
+        index colNorm = rasterLineMaxY(0, 0, mParam, row);
+        index colInv = rasterLineMaxY(mRows - 1, mCols - 1, 1 / mParam, row);
+
+        index col = std::min(colNorm, colInv);
+
+        return col < 0 ? 0 : col > mCols - 1 ? mCols - 1 : col;
+      }
+
+      case DTWConstraint::kSakoeChiba: {
+        index col = rasterLineMaxY(-mParam, mParam, mRows - 1 - mParam,
+                                   mCols - 1 + mParam, row);
+
+        return col < 0 ? 0 : col > mCols - 1 ? mCols - 1 : col;
+      }
+      }
+
+      return mCols - 1;
+    };
+  }; // struct Constraint
+};
+
+} // namespace algorithm
+} // namespace fluid
\ No newline at end of file
diff --git a/include/clients/nrt/DataSeriesClient.hpp b/include/clients/nrt/DataSeriesClient.hpp
index 7ab05224a..97128733c 100644
--- a/include/clients/nrt/DataSeriesClient.hpp
+++ b/include/clients/nrt/DataSeriesClient.hpp
@@ -14,6 +14,7 @@ under the European Union’s Horizon 2020 research and innovation programme
 #include "LabelSetClient.hpp"
 #include "NRTClient.hpp"
 #include "../common/SharedClientUtils.hpp"
+#include "../../algorithms/public/DTW.hpp"
 #include "../../algorithms/public/DataSetIdSequence.hpp"
 #include "../../data/FluidDataSeries.hpp"
 #include <sstream>
@@ -284,6 +285,91 @@ class DataSeriesClient
     return OK();
   }
 
+  MessageResult<FluidTensor<rt::string, 1>> kNearest(InputBufferPtr data,
+                                                     index nNeighbours) const
+  {
+    // check for nNeighbours > 0 and < size of DS
+    if (mAlgorithm.size() == 0)
+      return Error<FluidTensor<rt::string, 1>>(EmptyDataSeries);
+    if (nNeighbours > mAlgorithm.size())
+      return Error<FluidTensor<rt::string, 1>>(LargeK);
+    if (nNeighbours <= 0) return Error<FluidTensor<rt::string, 1>>(SmallK);
+
+    BufferAdaptor::ReadAccess buf(data.get());
+    if (!buf.exists()) return Error<FluidTensor<rt::string, 1>>(InvalidBuffer);
+    if (buf.numChans() < mAlgorithm.dims())
+      return Error<FluidTensor<rt::string, 1>>(WrongPointSize);
+
+    FluidTensor<const double, 2> series(buf.allFrames().transpose());
+
+    rt::vector<index>  indices(asUnsigned(mAlgorithm.size()));
+    rt::vector<double> distances(asUnsigned(mAlgorithm.size()));
+
+    std::iota(indices.begin(), indices.end(), 0);
+
+    auto ds = mAlgorithm.getData();
+
+    std::transform(
+        indices.begin(), indices.end(), distances.begin(),
+        [&series, &ds, this](index i) { return distance(series, ds[i], 2); });
+
+    std::sort(indices.begin(), indices.end(), [&distances](index a, index b) {
+      return distances[asUnsigned(a)] < distances[asUnsigned(b)];
+    });
+
+    FluidTensor<rt::string, 1> labels(nNeighbours);
+
+    std::transform(
+        indices.begin(), indices.begin() + nNeighbours, labels.begin(),
+        [this](index i) {
+          std::string const& id = mAlgorithm.getIds()[i];
+          return rt::string{id, 0, id.size(), FluidDefaultAllocator()};
+        });
+
+    return labels;
+  }
+
+  MessageResult<FluidTensor<double, 1>> kNearestDist(InputBufferPtr data,
+                                                     index nNeighbours) const
+  {
+    // check for nNeighbours > 0 and < size of DS
+    if (mAlgorithm.size() == 0)
+      return Error<FluidTensor<double, 1>>(EmptyDataSeries);
+    if (nNeighbours > mAlgorithm.size())
+      return Error<FluidTensor<double, 1>>(LargeK);
+    if (nNeighbours <= 0) return Error<FluidTensor<double, 1>>(SmallK);
+
+    BufferAdaptor::ReadAccess buf(data.get());
+    if (!buf.exists()) return Error<FluidTensor<double, 1>>(InvalidBuffer);
+    if (buf.numChans() < mAlgorithm.dims())
+      return Error<FluidTensor<double, 1>>(WrongPointSize);
+
+    FluidTensor<const double, 2> series(buf.allFrames().transpose());
+
+    rt::vector<index>  indices(asUnsigned(mAlgorithm.size()));
+    rt::vector<double> distances(asUnsigned(mAlgorithm.size()));
+
+    std::iota(indices.begin(), indices.end(), 0);
+
+    auto ds = mAlgorithm.getData();
+
+    std::transform(
+        indices.begin(), indices.end(), distances.begin(),
+        [&series, &ds, this](index i) { return distance(series, ds[i], 2); });
+
+    std::sort(indices.begin(), indices.end(), [&distances](index a, index b) {
+      return distances[asUnsigned(a)] < distances[asUnsigned(b)];
+    });
+
+    FluidTensor<double, 1> labels(nNeighbours);
+
+    std::transform(indices.begin(), indices.begin() + nNeighbours,
+                   labels.begin(),
+                   [&distances](index i) { return distances[i]; });
+
+    return labels;
+  }
+
   MessageResult<void> clear()
   {
     mAlgorithm = DataSeries(0);
@@ -321,6 +407,8 @@ class DataSeriesClient
         makeMessage("clear", &DataSeriesClient::clear),
         makeMessage("write", &DataSeriesClient::write),
         makeMessage("read", &DataSeriesClient::read),
+        makeMessage("kNearest", &DataSeriesClient::kNearest),
+        makeMessage("kNearestDist", &DataSeriesClient::kNearestDist),
         makeMessage("getIds", &DataSeriesClient::getIds),
         makeMessage("getDataSet", &DataSeriesClient::getDataSet));
   }
@@ -349,6 +437,13 @@ class DataSeriesClient
 
     return ds;
   }
+
+  double distance(InputRealMatrixView x1, InputRealMatrixView x2, index p) const
+  {
+    algorithm::DTW dtw;
+    return dtw.process(x1, x2, algorithm::DTWConstraint::kSakoeChiba,
+                       std::min(x1.size(), x2.size()) / 4);
+  }
 };
 
 } // namespace dataseries