diff --git a/river/metrics/__init__.py b/river/metrics/__init__.py
index 5a1e102756..fa62ee9a7e 100644
--- a/river/metrics/__init__.py
+++ b/river/metrics/__init__.py
@@ -60,6 +60,7 @@
 from .recall import MacroRecall, MicroRecall, Recall, WeightedRecall
 from .report import ClassificationReport
 from .roc_auc import ROCAUC
+from .rolling_pr_auc import RollingPRAUC
 from .rolling_roc_auc import RollingROCAUC
 from .silhouette import Silhouette
 from .smape import SMAPE
@@ -108,6 +109,7 @@
     "FowlkesMallows",
     "RMSLE",
     "ROCAUC",
+    "RollingPRAUC",
     "RollingROCAUC",
     "R2",
     "Precision",
diff --git a/river/metrics/efficient_rollingprauc/__init__.py b/river/metrics/efficient_rollingprauc/__init__.py
new file mode 100644
index 0000000000..cac3a79140
--- /dev/null
+++ b/river/metrics/efficient_rollingprauc/__init__.py
@@ -0,0 +1,5 @@
+from __future__ import annotations
+
+from .efficient_rollingprauc import EfficientRollingPRAUC
+
+__all__ = ["EfficientRollingPRAUC"]
diff --git a/river/metrics/efficient_rollingprauc/cpp/RollingPRAUC.cpp b/river/metrics/efficient_rollingprauc/cpp/RollingPRAUC.cpp
new file mode 100644
index 0000000000..568d0b4bc1
--- /dev/null
+++ b/river/metrics/efficient_rollingprauc/cpp/RollingPRAUC.cpp
@@ -0,0 +1,150 @@
+#include "RollingPRAUC.hpp"
+
+#include <limits>
+#include <stdlib.h>
+
+namespace rollingprauc {
+
+RollingPRAUC::RollingPRAUC(): positiveLabel{1}, windowSize{1000}, positives{0} {
+}
+
+RollingPRAUC::RollingPRAUC(int positiveLabel, long unsigned windowSize):
+    positiveLabel{positiveLabel}, windowSize{windowSize}, positives{0} {
+}
+
+void RollingPRAUC::update(int label, double score) {
+    if (this->window.size() == this->windowSize)
+        this->removeLast();
+
+    this->insert(label, score);
+
+    return;
+}
+
+void RollingPRAUC::revert(int label, double score) {
+    int normalizedLabel = 0;
+    if (label == this->positiveLabel)
+        normalizedLabel = 1;
+
+    std::deque<std::tuple<double, int>>::const_iterator it{this->window.cbegin()};
+    for (; it != this->window.cend(); ++it)
+        if (std::get<0>(*it) == score && std::get<1>(*it) == normalizedLabel)
+            break;
+
+    if (it == this->window.cend())
+        return;
+
+    if (normalizedLabel)
+        this->positives--;
+
+    this->window.erase(it);
+
+    std::multiset<std::tuple<double, int>>::const_iterator itr{
+        this->orderedWindow.find(std::make_tuple(score, label))
+    };
+    this->orderedWindow.erase(itr);
+
+    return;
+}
+
+double RollingPRAUC::get() const {
+    unsigned long windowSize{this->window.size()};
+
+    // If there is only one class in the window, it will lead to a
+    // division by zero. So, zero is returned.
+    if (!this->positives || !(windowSize - this->positives))
+        return 0;
+
+    unsigned long fp{windowSize - this->positives};
+    unsigned long tp{this->positives}, tpPrev{tp};
+
+    double auc{0}, scorePrev{std::numeric_limits<double>::max()};
+
+    double prec{tp / (double) (tp + fp)}, precPrev{prec};
+
+    std::multiset<std::tuple<double, int>>::const_iterator it{this->orderedWindow.begin()};
+    double score;
+    int label;
+
+    for (; it != this->orderedWindow.end(); ++it) {
+        score = std::get<0>(*it);
+        label = std::get<1>(*it);
+
+        if (score != scorePrev) {
+            prec = tp / (double) (tp + fp);
+
+            if (precPrev > prec)
+                prec = precPrev; // Monotonic. decreasing
+
+            auc += this->trapzArea(tp, tpPrev, prec, precPrev);
+
+            scorePrev = score;
+            tpPrev = tp;
+            precPrev = prec;
+        }
+
+        if (label) tp--;
+        else fp--;
+    }
+
+    auc += this->trapzArea(tp, tpPrev, 1.0, precPrev);
+
+    return auc / this->positives; // Scale the x axis
+}
+
+void RollingPRAUC::insert(int label, double score) {
+    // Normalize label to 0 (negative) or 1 (positive)
+    int l = 0;
+    if (label == this->positiveLabel) {
+        l = 1;
+        this->positives++;
+    }
+
+    this->window.emplace_back(score, l);
+    this->orderedWindow.emplace(score, l);
+
+    return;
+}
+
+void RollingPRAUC::removeLast() {
+    std::tuple<double, int> last{this->window.front()};
+
+    if (std::get<1>(last))
+        this->positives--;
+
+    this->window.pop_front();
+
+    // Erase using a iterator to avoid multiple erases with equivalent instances
+    std::multiset<std::tuple<double, int>>::iterator it{
+        this->orderedWindow.find(last)
+    };
+    this->orderedWindow.erase(it);
+
+    return;
+}
+
+std::vector<int> RollingPRAUC::getTrueLabels() const {
+    std::vector<int> trueLabels;
+
+    std::deque<std::tuple<double, int>>::const_iterator it{this->window.begin()};
+    for (; it != this->window.end(); ++it)
+        trueLabels.push_back(std::get<1>(*it));
+
+    return trueLabels;
+}
+
+std::vector<double> RollingPRAUC::getScores() const {
+    std::vector<double> scores;
+
+    std::deque<std::tuple<double, int>>::const_iterator it{this->window.begin()};
+    for (; it != this->window.end(); ++it)
+        scores.push_back(std::get<0>(*it));
+
+    return scores;
+}
+
+double RollingPRAUC::trapzArea(double x1, double x2, double y1, double y2) const {
+    return abs(x1 - x2) * (y1 + y2) / 2;
+}
+
+} // namespace rollingprauc
diff --git a/river/metrics/efficient_rollingprauc/cpp/RollingPRAUC.hpp b/river/metrics/efficient_rollingprauc/cpp/RollingPRAUC.hpp
new file mode 100644
index 0000000000..e47e423079
--- /dev/null
+++ b/river/metrics/efficient_rollingprauc/cpp/RollingPRAUC.hpp
@@ -0,0 +1,59 @@
+#ifndef ROLLINGPRAUC_HPP
+#define ROLLINGPRAUC_HPP
+
+#include <deque>
+#include <set>
+#include <tuple>
+#include <vector>
+
+namespace rollingprauc {
+
+class RollingPRAUC {
+    public:
+        RollingPRAUC();
+        RollingPRAUC(const int positiveLabel, const long unsigned windowSize);
+
+        virtual ~RollingPRAUC() = default;
+
+        // Calls insert() and removeLast() if needed
+        virtual void update(const int label, const double score);
+
+        // Erase the most recent instance with content equal to params
+        virtual void revert(const int label, const double score);
+
+        // Calculates the PRAUC and returns it
+        virtual double get() const;
+
+        // Returns y_true as a vector
+        virtual std::vector<int> getTrueLabels() const;
+
+        // Returns y_score as a vector
+        virtual std::vector<double> getScores() const;
+
+    private:
+        // Insert instance based on params
+        virtual void insert(const int label, const double score);
+
+        // Remove oldest instance
+        virtual void removeLast();
+
+        // Calculates the trapezoid area
+        double trapzArea(double x1, double x2, double y1, double y2) const;
+
+        int positiveLabel;
+
+        std::size_t windowSize;
+        std::size_t positives;
+
+        // window maintains a queue of the instances to store the temporal
+        // aspect of the stream. Using deque to allow revert()
+        std::deque<std::tuple<double, int>> window;
+
+        // orderedWindow maintains a multiset (implemented as a tree)
+        // to store the ordered instances
+        std::multiset<std::tuple<double, int>> orderedWindow;
+};
+
+} // namespace rollingprauc
+
+#endif
diff --git a/river/metrics/efficient_rollingprauc/efficient_rollingprauc.pxd b/river/metrics/efficient_rollingprauc/efficient_rollingprauc.pxd
new file mode 100644
index 0000000000..c9a0d8f5c0
--- /dev/null
+++ b/river/metrics/efficient_rollingprauc/efficient_rollingprauc.pxd
@@ -0,0 +1,13 @@
+from libcpp.vector cimport vector
+
+cdef extern from "cpp/RollingPRAUC.cpp":
+    pass
+
+cdef extern from "cpp/RollingPRAUC.hpp" namespace "rollingprauc":
+    cdef cppclass RollingPRAUC:
+        RollingPRAUC(int positiveLabel, int windowSize) except +
+        void update(int label, double score)
+        void revert(int label, double score)
+        double get()
+        vector[int] getTrueLabels()
+        vector[double] getScores()
diff --git a/river/metrics/efficient_rollingprauc/efficient_rollingprauc.pyx b/river/metrics/efficient_rollingprauc/efficient_rollingprauc.pyx
new file mode 100644
index 0000000000..1c8cb4123e
--- /dev/null
+++ b/river/metrics/efficient_rollingprauc/efficient_rollingprauc.pyx
@@ -0,0 +1,55 @@
+# distutils: language = c++
+# distutils: extra_compile_args = "-std=c++11"
+
+import cython
+
+from .efficient_rollingprauc cimport RollingPRAUC as CppRollingPRAUC
+
+cdef class EfficientRollingPRAUC:
+    cdef cython.int positiveLabel
+    cdef cython.ulong windowSize
+    cdef CppRollingPRAUC* rollingprauc
+
+    def __cinit__(self, cython.int positiveLabel, cython.ulong windowSize):
+        self.positiveLabel = positiveLabel
+        self.windowSize = windowSize
+        self.rollingprauc = new CppRollingPRAUC(positiveLabel, windowSize)
+
+    def __dealloc__(self):
+        if not self.rollingprauc == NULL:
+            del self.rollingprauc
+
+    def update(self, label, score):
+        self.rollingprauc.update(label, score)
+
+    def revert(self, label, score):
+        self.rollingprauc.revert(label, score)
+
+    def get(self):
+        return self.rollingprauc.get()
+
+    def __getnewargs_ex__(self):
+        # Pickle will use this function to pass the arguments to __new__
+        return (self.positiveLabel, self.windowSize),{}
+
+    def __getstate__(self):
+        """
+            On pickling, the true labels and scores of the instances in the
+            window will be dumped
+        """
+        return (self.rollingprauc.getTrueLabels(), self.rollingprauc.getScores())
+
+    def __setstate__(self, state):
+        """
+            On unpickling, the state parameter will have the true labels
+            and scores, this function updates the rollingprauc with them
+        """
+
+        # Labels returned by __getstate__ are normalized (0 or 1)
+        labels, scores = state
+
+        for label, score in zip(labels, scores):
+            # If label is 1, update with the positive label defined by the constructor
+            # Else, update with a negative label
+            l = self.positiveLabel if label else int(not self.positiveLabel)
+            self.update(l, score)
diff --git a/river/metrics/rolling_pr_auc.py b/river/metrics/rolling_pr_auc.py
new file mode 100644
index 0000000000..d893eb843d
--- /dev/null
+++ b/river/metrics/rolling_pr_auc.py
@@ -0,0 +1,84 @@
+from __future__ import annotations
+
+from river import metrics, utils
+
+from .efficient_rollingprauc import EfficientRollingPRAUC
+
+__all__ = ["RollingPRAUC"]
+
+
+class RollingPRAUC(metrics.base.BinaryMetric):
+    """Rolling version of the Area Under the Precision-Recall Area Under Curve
+    metric.
+
+    The RollingPRAUC calculates the AUC-PR using the instances in its window
+    of size S. It keeps a queue of the instances, when an instance is added
+    and the queue length is equal to S, the last instance is removed.
+
+    The AUC-PR is suitable for evaluating models under unbalanced environments.
+    For now, the implementation can deal only with binary scenarios.
+
+    Internally, this class maintains a self-balancing binary search tree to
+    efficiently and precisely (i.e., the result is not an approximation)
+    compute the AUC-PR considering the current window.
+
+    This implementation is based on the paper "Efficient Prequential AUC-PR
+    Computation" (Gomes, Grégio, Alves, and Almeida, 2023):
+    https://doi.org/10.1109/ICMLA58977.2023.00335.
+
+
+    Parameters
+    ----------
+    window_size
+        The max length of the window.
+    pos_val
+        Value to treat as "positive".
+
+    Examples
+    --------
+
+    >>> from river import metrics
+
+    >>> y_true = [ 0,  1,  0,  1,  0,  1,  0,  0,   1,  1]
+    >>> y_pred = [.3, .5, .5, .7, .1, .3, .1, .4, .35, .8]
+
+    >>> metric = metrics.RollingPRAUC(window_size=4)
+
+    >>> for yt, yp in zip(y_true, y_pred):
+    ...     metric.update(yt, yp)
+
+    >>> metric
+    RollingPRAUC: 83.33%
+
+    """
+
+    def __init__(self, window_size=1000, pos_val=True):
+        self.window_size = window_size
+        self.pos_val = pos_val
+        self._metric = EfficientRollingPRAUC(pos_val, window_size)
+
+    def works_with(self, model) -> bool:
+        return (
+            super().works_with(model)
+            or utils.inspect.isanomalydetector(model)
+            or utils.inspect.isanomalyfilter(model)
+        )
+
+    def update(self, y_true, y_pred):
+        p_true = y_pred.get(True, 0.0) if isinstance(y_pred, dict) else y_pred
+        self._metric.update(y_true, p_true)
+
+    def revert(self, y_true, y_pred):
+        p_true = y_pred.get(True, 0.0) if isinstance(y_pred, dict) else y_pred
+        self._metric.revert(y_true, p_true)
+
+    @property
+    def requires_labels(self) -> bool:
+        return False
+
+    @property
+    def works_with_weights(self) -> bool:
+        return False
+
+    def get(self):
+        return self._metric.get()
diff --git a/river/metrics/test_metrics.py b/river/metrics/test_metrics.py
index fa66a9fc6b..9f55d3a615 100644
--- a/river/metrics/test_metrics.py
+++ b/river/metrics/test_metrics.py
@@ -116,6 +116,24 @@ def roc_auc_score(y_true, y_score):
     return sk_metrics.roc_auc_score(y_true, scores)
 
 
+def pr_auc_score(y_true, y_score):
+    """
+    This function is a wrapper to the scikit-learn precision_recall_curve and
+    auc functions. Returns 0 if y_true has only one class.
+    """
+    nonzero = np.count_nonzero(y_true)
+    if nonzero == 0 or nonzero == len(y_true):
+        return 0
+
+    scores = [s[True] for s in y_score]
+    precision, recall, _ = sk_metrics.precision_recall_curve(y_true, scores)
+
+    # Monotonic. decreasing
+    precision = np.maximum.accumulate(precision)
+
+    return sk_metrics.auc(recall, precision)
+
+
 TEST_CASES = [
     (metrics.Accuracy(), sk_metrics.accuracy_score),
     (metrics.Precision(), partial(sk_metrics.precision_score, zero_division=0)),
@@ -210,6 +228,7 @@ def roc_auc_score(y_true, y_score):
     (metrics.MicroJaccard(), partial(sk_metrics.jaccard_score, average="micro")),
     (metrics.WeightedJaccard(), partial(sk_metrics.jaccard_score, average="weighted")),
     (metrics.RollingROCAUC(), roc_auc_score),
+    (metrics.RollingPRAUC(), pr_auc_score),
 ]
 
 # HACK: not sure why this is needed, see this CI run https://github.com/online-ml/river/runs/7992357532?check_suite_focus=true