diff --git a/sigllm/pipelines/detector/mistral_detector_kmeans.json b/sigllm/pipelines/detector/mistral_detector_kmeans.json
new file mode 100644
index 0000000..53afb6f
--- /dev/null
+++ b/sigllm/pipelines/detector/mistral_detector_kmeans.json
@@ -0,0 +1,101 @@
+{
+    "primitives": [
+        "mlstars.custom.timeseries_preprocessing.time_segments_aggregate",
+        "sklearn.impute.SimpleImputer",
+        "sigllm.primitives.transformation.Scalar2Cluster",
+        "mlstars.custom.timeseries_preprocessing.rolling_window_sequences",
+        "sigllm.primitives.transformation.format_as_string",
+        "sigllm.primitives.forecasting.huggingface.HF",
+        "sigllm.primitives.transformation.format_as_integer",
+        "sigllm.primitives.transformation.Cluster2Scalar",
+        "sigllm.primitives.transformation.Cluster2Scalar",
+        "sigllm.primitives.postprocessing.aggregate_rolling_window",
+        "numpy.reshape",
+        "orion.primitives.timeseries_errors.regression_errors",
+        "orion.primitives.timeseries_anomalies.find_anomalies"
+    ],
+    "init_params": {
+        "mlstars.custom.timeseries_preprocessing.time_segments_aggregate#1": {
+            "time_column": "timestamp",
+            "interval": 21600,
+            "method": "mean"
+        },
+        "sigllm.primitives.transformation.Scalar2Cluster#1": {
+            "n_clusters": 100,
+            "fit_fraction": 1.0
+        },
+        "mlstars.custom.timeseries_preprocessing.rolling_window_sequences#1": {
+            "target_column": 0,
+            "window_size": 140,
+            "target_size": 1
+        },
+        "sigllm.primitives.transformation.format_as_string#1": {
+            "space": false
+        },
+        "sigllm.primitives.forecasting.huggingface.HF#1": {
+            "name": "mistralai/Mistral-7B-Instruct-v0.2",
+            "steps": 5
+        },
+        "sigllm.primitives.transformation.format_as_integer#1": {
+            "trunc": 1,
+            "errors": "coerce"
+        },
+        "sigllm.primitives.postprocessing.aggregate_rolling_window#1": {
+            "agg": "median"
+        },
+        "orion.primitives.timeseries_anomalies.find_anomalies#1": {
+            "window_size_portion": 0.3,
+            "window_step_size_portion": 0.1,
+            "fixed_threshold": true
+        }
+    },
+    "input_names": {
+        "sigllm.primitives.transformation.Scalar2Cluster#1": {
+            "X": "y"
+        },
+        "sigllm.primitives.transformation.Cluster2Scalar#1": {
+            "X": "y"
+        },
+        "sigllm.primitives.transformation.format_as_integer#1": {
+            "X": "y_hat"
+        },
+        "sigllm.primitives.transformation.Cluster2Scalar#2": {
+            "X": "y_hat"
+        },
+        "sigllm.primitives.postprocessing.aggregate_rolling_window#1": {
+            "y": "y_hat"
+        },
+        "numpy.reshape#1": {
+            "X": "y_hat"
+        },
+        "orion.primitives.timeseries_anomalies.find_anomalies#1": {
+            "index": "target_index"
+        }
+    },
+    "output_names": {
+        "sklearn.impute.SimpleImputer#1": {
+            "X": "y"
+        },
+        "sigllm.primitives.forecasting.huggingface.HF#1": {
+            "y": "y_hat"
+        },
+        "sigllm.primitives.transformation.format_as_integer#1": {
+            "X": "y_hat"
+        },
+        "sigllm.primitives.transformation.Cluster2Scalar#1": {
+            "X": "y"
+        },
+        "sigllm.primitives.transformation.Cluster2Scalar#2": {
+            "X": "y_hat"
+        },
+        "sigllm.primitives.postprocessing.aggregate_rolling_window#1": {
+            "y": "y_hat"
+        },
+        "numpy.reshape#1": {
+            "X": "y_hat"
+        },
+        "orion.primitives.timeseries_anomalies.find_anomalies#1": {
+            "y": "anomalies"
+        }
+    }
+}
diff --git a/sigllm/primitives/jsons/sigllm.primitives.transformation.Cluster2Scalar.json b/sigllm/primitives/jsons/sigllm.primitives.transformation.Cluster2Scalar.json
new file mode 100644
index 0000000..24401a7
--- /dev/null
+++ b/sigllm/primitives/jsons/sigllm.primitives.transformation.Cluster2Scalar.json
@@ -0,0 +1,32 @@
+{
+    "name": "sigllm.primitives.transformation.Cluster2Scalar",
+    "contributors": [
+        "Allen Baranov <baranov@mit.edu>"
+    ],
+    "description": "Transform cluster indices back to float values using centroids.",
+    "classifiers": {
+        "type": "preprocessor",
+        "subtype": "transformer"
+    },
+    "modalities": [],
+    "primitive": "sigllm.primitives.transformation.Cluster2Scalar",
+    "produce": {
+        "method": "transform",
+        "args": [
+            {
+                "name": "X",
+                "type": "ndarray"
+            },
+            {
+                "name": "centroids",
+                "type": "list"
+            }
+        ],
+        "output": [
+            {
+                "name": "X",
+                "type": "ndarray"
+            }
+        ]
+    }
+}
diff --git a/sigllm/primitives/jsons/sigllm.primitives.transformation.Scalar2Cluster.json b/sigllm/primitives/jsons/sigllm.primitives.transformation.Scalar2Cluster.json
new file mode 100644
index 0000000..3d714bf
--- /dev/null
+++ b/sigllm/primitives/jsons/sigllm.primitives.transformation.Scalar2Cluster.json
@@ -0,0 +1,53 @@
+{
+    "name": "sigllm.primitives.transformation.Scalar2Cluster",
+    "contributors": [
+        "Allen Baranov <baranov@mit.edu>"
+    ],
+    "description": "Transform float values into cluster indices using K-means.",
+    "classifiers": {
+        "type": "preprocessor",
+        "subtype": "transformer"
+    },
+    "modalities": [],
+    "primitive": "sigllm.primitives.transformation.Scalar2Cluster",
+    "fit": {
+        "method": "fit",
+        "args": [
+            {
+                "name": "X",
+                "type": "ndarray"
+            }
+        ]
+    },
+    "produce": {
+        "method": "transform",
+        "args": [
+            {
+                "name": "X",
+                "type": "ndarray"
+            }
+        ],
+        "output": [
+            {
+                "name": "X",
+                "type": "ndarray"
+            },
+            {
+                "name": "centroids",
+                "type": "list"
+            }
+        ]
+    },
+    "hyperparameters": {
+        "fixed": {
+            "n_clusters": {
+                "type": "int",
+                "default": 100
+            },
+            "fit_fraction": {
+                "type": "float",
+                "default": 1.0
+            }
+        }
+    }
+}
diff --git a/sigllm/primitives/transformation.py b/sigllm/primitives/transformation.py
index 14541b2..0381ebe 100644
--- a/sigllm/primitives/transformation.py
+++ b/sigllm/primitives/transformation.py
@@ -4,6 +4,7 @@
 import re
 
 import numpy as np
+from sklearn.cluster import KMeans
 
 
 def format_as_string(X, sep=',', space=False, single=False):
@@ -96,7 +97,7 @@ def format_as_integer(X, sep=',', trunc=None, errors='ignore'):
             to `'ignore'`.
             - If 'ignore', then invalid values will be ignored in the result.
             - If 'filter', then invalid values will be filtered out of the string.
-            - If 'raise', then encountering invalud values will raise an exception.
+            - If 'raise', then encountering invalid values will raise an exception.
             - If 'coerce', then invalid values will be set as NaN.
 
     Returns:
@@ -188,3 +189,102 @@ def transform(self, X, minimum=0, decimal=2):
         values = X * 10 ** (-decimal)
 
         return values + minimum
+
+
+class Scalar2Cluster:
+    """Convert an array of float values to cluster indices using K-means.
+
+    Fits K-means on the input data and maps each value to the index of
+    its nearest centroid. Centroids are sorted in ascending order so that
+    cluster index 0 corresponds to the smallest centroid value.
+
+    Args:
+        n_clusters (int):
+            Number of K-means clusters. Default to ``100``.
+        fit_fraction (float):
+            Fraction of data to use for fitting K-means (0 < fit_fraction <= 1).
+            If less than 1, only the first fit_fraction of rows are used for fitting.
+            Default to ``1.0`` (use all data).
+    """
+
+    def __init__(self, n_clusters=100, fit_fraction=1.0):
+        self.n_clusters = n_clusters
+        self.fit_fraction = fit_fraction
+        self.centroids = None
+
+    def fit(self, X):
+        """Fit K-means on the data and store sorted centroids.
+
+        Args:
+            X (ndarray):
+                2-D array of shape (n_samples, n_features)
+
+        Returns:
+            No output. The method stores the fitted centroids in the
+            class instance instead.
+        """
+        n_samples = X.shape[0]
+        n_fit = max(1, int(n_samples * self.fit_fraction))
+        X_fit = X[:n_fit]
+        
+        centroids_list = []
+        for col in X_fit.T:
+            n_unique = len(np.unique(col))
+            if self.n_clusters >= n_unique:
+                centroids = np.sort(np.unique(col))
+            else:
+                kmeans = KMeans(n_clusters=self.n_clusters, random_state=0, n_init=10)
+                kmeans.fit(col.reshape(-1, 1))
+                centroids = np.sort(kmeans.cluster_centers_.ravel())
+            centroids_list.append(centroids)
+
+        self.centroids = centroids_list
+
+    def transform(self, X):
+        """Map each value to its nearest centroid index.
+
+        Args:
+            X (ndarray):
+                2-D array of shape ``(n_samples, n_features)``.
+
+        Returns:
+            X (ndarray):
+                Integer cluster labels with the same shape as input.
+            centroids (list of ndarray):
+                Sorted centroid arrays, one per column.
+        """
+        labels_list = []
+        for i, col in enumerate(X.T):
+            centroids = self.centroids[i]
+            col_labels = np.argmin(np.abs(col[:, None] - centroids[None, :]), axis=1)
+            labels_list.append(col_labels)
+
+        labels = (
+            np.column_stack(labels_list) if len(labels_list) > 1 else labels_list[0].reshape(-1, 1)
+        )
+        return labels, self.centroids
+
+
+class Cluster2Scalar:
+    """Convert cluster indices back to float values using centroids.
+
+    Maps an array of integer cluster indices to the corresponding
+    centroid values produced by Scalar2Cluster.
+    """
+
+    def transform(self, X, centroids):
+        """Convert cluster indices to centroid float values.
+
+        Args:
+            X (ndarray):
+                Integer cluster labels.
+            centroids (list of ndarray):
+                Sorted centroid arrays from Scalar2Cluster.
+
+        Returns:
+            ndarray:
+                Float values corresponding to the centroid of each label.
+        """
+        base_centroids = np.asarray(centroids[0])
+        idx = np.clip(X.astype(int), 0, len(base_centroids) - 1)
+        return np.take(base_centroids, idx)
diff --git a/tests/primitives/test_transformation.py b/tests/primitives/test_transformation.py
index 4c62464..5140b7f 100644
--- a/tests/primitives/test_transformation.py
+++ b/tests/primitives/test_transformation.py
@@ -4,7 +4,9 @@
 import pytest
 
 from sigllm.primitives.transformation import (
+    Cluster2Scalar,
     Float2Scalar,
+    Scalar2Cluster,
     Scalar2Float,
     _from_string_to_integer,
     format_as_integer,
@@ -341,3 +343,86 @@ def test_float2scalar_scalar2float_integration():
     output = scalar2float.transform(transformed, minimum, decimal)
 
     np.testing.assert_allclose(output, expected, rtol=1e-2)
+
+
+class Scalar2ClusterTest(unittest.TestCase):
+    """Tests for Scalar2Cluster (K-means binning)"""
+
+    def test_fit_transform_single_column(self):
+        s2c = Scalar2Cluster(n_clusters=3)
+        X = np.array([[1.0], [2.0], [3.0], [10.0], [11.0], [12.0]])
+        s2c.fit(X)
+        labels, centroids = s2c.transform(X)
+        self.assertIsInstance(centroids, list)
+        self.assertEqual(len(centroids), 1)
+        np.testing.assert_array_equal(np.sort(centroids[0]), centroids[0])
+        self.assertEqual(labels.shape, (6, 1))
+
+    def test_fit_transform_multi_column(self):
+        s2c = Scalar2Cluster(n_clusters=2)
+        X = np.array([[1.0, 0.0], [2.0, 1.0], [10.0, 10.0], [11.0, 11.0]])
+        s2c.fit(X)
+        labels, centroids = s2c.transform(X)
+        self.assertEqual(len(centroids), 2)
+        self.assertEqual(labels.shape, (4, 2))
+        for i in range(2):
+            np.testing.assert_array_equal(np.sort(centroids[i]), centroids[i])
+            self.assertTrue(np.all(labels[:, i] >= 0))
+            self.assertTrue(np.all(labels[:, i] < 2))
+
+    def test_n_clusters_uses_unique_values(self):
+        s2c = Scalar2Cluster(n_clusters=10)
+        X = np.array([[1.0], [2.0], [3.0]])
+        s2c.fit(X)
+        labels, centroids = s2c.transform(X)
+        np.testing.assert_array_equal(centroids[0], np.array([1.0, 2.0, 3.0]))
+        np.testing.assert_array_equal(labels.ravel(), np.array([0, 1, 2]))
+
+    def test_labels_in_valid_range(self):
+        s2c = Scalar2Cluster(n_clusters=5)
+        np.random.seed(42)
+        X = np.random.randn(100, 1) * 10
+        s2c.fit(X)
+        labels, _ = s2c.transform(X)
+        self.assertTrue(np.all(labels >= 0))
+        self.assertTrue(np.all(labels < 5))
+        self.assertTrue(np.all(labels == labels.astype(int)))
+
+
+class Cluster2ScalarTest(unittest.TestCase):
+    """Tests for Cluster2Scalar (transforming clusters back to scalars)"""
+
+    def test_transform_basic(self):
+        c2s = Cluster2Scalar()
+        centroids = [np.array([0.0, 1.0, 2.0])]
+        labels = np.array([[0], [1], [2], [1], [0]])
+        out = c2s.transform(labels, centroids)
+        np.testing.assert_array_equal(out, np.array([[0.0], [1.0], [2.0], [1.0], [0.0]]))
+
+    def test_transform_boundary_labels(self):
+        c2s = Cluster2Scalar()
+        centroids = [np.array([-1.0, 0.0, 1.0])]
+        labels = np.array([[0], [2]])
+        out = c2s.transform(labels, centroids)
+        np.testing.assert_array_equal(out, np.array([[-1.0], [1.0]]))
+
+    def test_transform_clips_out_of_range(self):
+        c2s = Cluster2Scalar()
+        centroids = [np.array([0.0, 1.0])]
+        labels = np.array([[0], [1], [5], [-1]])
+        out = c2s.transform(labels, centroids)
+        np.testing.assert_array_equal(out, np.array([[0.0], [1.0], [1.0], [0.0]]))
+
+
+def test_scalar2cluster_cluster2scalar_roundtrip():
+    """Round-trip: Scalar2Cluster -> Cluster2Scalar recovers centroid values."""
+    s2c = Scalar2Cluster(n_clusters=4)
+    np.random.seed(0)
+    X = np.random.rand(50, 1) * 10
+    s2c.fit(X)
+    labels, centroids = s2c.transform(X)
+
+    c2s = Cluster2Scalar()
+    recovered = c2s.transform(labels, centroids)
+    expected = np.array([[centroids[0][label]] for label in labels.ravel()])
+    np.testing.assert_allclose(recovered, expected)
diff --git a/tutorials/pipelines/detector-pipeline-kmeans.ipynb b/tutorials/pipelines/detector-pipeline-kmeans.ipynb
new file mode 100644
index 0000000..bea7281
--- /dev/null
+++ b/tutorials/pipelines/detector-pipeline-kmeans.ipynb
@@ -0,0 +1,1200 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings; warnings.simplefilter('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook requires **gpu** to run. See [mistral documentation](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) for memory requirements. \n",
+    "\n",
+    "This is the **K-means binning** variant of the detector pipeline. Instead of decimal scaling, it discretises the signal into K-means cluster indices."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1624, 2)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from orion.data import load_signal\n",
+    "\n",
+    "data = load_signal('exchange-2_cpm_results')\n",
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.plot(data['value']);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "if you want a quick test of how this pipeline works, uncomment the cell below\n",
+    "to save time. We will look at a small segment of the time series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "start = 900\n",
+    "end = start + 1000\n",
+    "\n",
+    "data = data.iloc[start: end]\n",
+    "\n",
+    "plt.plot(data['value']);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-02-16 15:35:00.364616: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
+      "2026-02-16 15:35:00.397314: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "2026-02-16 15:35:00.397347: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+      "2026-02-16 15:35:00.397379: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "2026-02-16 15:35:00.404260: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+      "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2026-02-16 15:35:01.067427: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
+      "Loading checkpoint shards: 100%|██████████| 3/3 [00:04<00:00,  1.51s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from mlblocks import MLPipeline \n",
+    "\n",
+    "pipeline_name = 'mistral_detector_kmeans'\n",
+    "\n",
+    "pipeline = MLPipeline(pipeline_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading checkpoint shards: 100%|██████████| 3/3 [00:04<00:00,  1.52s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "hyperparameters = {\n",
+    "    \"mlstars.custom.timeseries_preprocessing.time_segments_aggregate#1\": {\n",
+    "        \"interval\": 3600\n",
+    "    },\n",
+    "    \"sigllm.primitives.transformation.Scalar2Cluster#1\": {\n",
+    "        \"n_clusters\": 100\n",
+    "    },\n",
+    "    \"sigllm.primitives.forecasting.custom.rolling_window_sequences#1\": {\n",
+    "        \"window_size\": 50\n",
+    "    },\n",
+    "    \"sigllm.primitives.forecasting.huggingface.HF#1\": {\n",
+    "        \"samples\": 2\n",
+    "    },\n",
+    "    \"sigllm.primitives.transformation.format_as_integer#1\": {\n",
+    "        \"trunc\": 1,\n",
+    "        \"errors\": \"coerce\"\n",
+    "    },\n",
+    "    \"orion.primitives.timeseries_anomalies.find_anomalies#1\": {\n",
+    "        \"window_size_portion\": 0.3,\n",
+    "        \"window_step_size_portion\": 0.1,\n",
+    "        \"fixed_threshold\": True,\n",
+    "        \"anomaly_padding\": 20\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "pipeline.set_hyperparameters(hyperparameters)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### step-by-step execution\n",
+    "\n",
+    "MLPipelines are compose of a squence of primitives, these primitives apply tranformation and calculation operations to the data and updates the variables within the pipeline. To view the primitives used by the pipeline, we access its primtivies attribute.\n",
+    "\n",
+    "The mistral-detector-kmeans contains 12 primitives. we will observe how the context (which are the variables held within the pipeline) are updated after the execution of each primitive."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['mlstars.custom.timeseries_preprocessing.time_segments_aggregate',\n",
+       " 'sklearn.impute.SimpleImputer',\n",
+       " 'sigllm.primitives.transformation.Scalar2Cluster',\n",
+       " 'sigllm.primitives.forecasting.custom.rolling_window_sequences',\n",
+       " 'sigllm.primitives.transformation.format_as_string',\n",
+       " 'sigllm.primitives.forecasting.huggingface.HF',\n",
+       " 'sigllm.primitives.transformation.format_as_integer',\n",
+       " 'sigllm.primitives.transformation.Cluster2Scalar',\n",
+       " 'sigllm.primitives.postprocessing.aggregate_rolling_window',\n",
+       " 'numpy.reshape',\n",
+       " 'orion.primitives.timeseries_errors.regression_errors',\n",
+       " 'orion.primitives.timeseries_anomalies.find_anomalies']"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pipeline.primitives"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### time segments aggregate\n",
+    "this primitive creates an equi-spaced time series by aggregating values over fixed specified interval.\n",
+    "\n",
+    "* **input**: `X` which is an n-dimensional sequence of values.\n",
+    "* **output**:\n",
+    "    * `X` sequence of aggregated values, one column for each aggregation method.\n",
+    "    * `index` sequence of index values (first index of each aggregated segment)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['X', 'index'])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 0\n",
+    "context = pipeline.fit(data, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry at 1312714801 has value [0.58756715]\n",
+      "entry at 1312718401 has value [0.49446836]\n",
+      "entry at 1312722001 has value [0.48571948]\n",
+      "entry at 1312725601 has value [0.34325765]\n",
+      "entry at 1312729201 has value [0.32660771]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i, x in list(zip(context['index'], context['X']))[:5]:\n",
+    "    print(\"entry at {} has value {}\".format(i, x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(747, 1)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['X'].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "notice that the dimension of `X` is now different from the original shape, which was (1624, 2)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### SimpleImputer\n",
+    "this primitive is an imputation transformer for filling missing values.\n",
+    "\n",
+    "* **input**: `X` which is an n-dimensional sequence of values.\n",
+    "* **output**: `y` which is a transformed version of 'X'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['index', 'X', 'y'])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 1\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Scalar2Cluster\n",
+    "this primitive fits K-means on the data and maps each float value to the index of its nearest centroid. centroids are sorted so that index 0 is the smallest value.\n",
+    "\n",
+    "* **input**: `y` which is an n-dimensional sequence of float values.\n",
+    "* **output**:\n",
+    "    * `X` integer cluster labels.\n",
+    "    * `centroids` sorted centroid arrays."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['index', 'X', 'y', 'centroids'])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 2\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry at 1312714801 has value [78]\n",
+      "entry at 1312718401 has value [65]\n",
+      "entry at 1312722001 has value [64]\n",
+      "entry at 1312725601 has value [43]\n",
+      "entry at 1312729201 has value [40]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i, x in list(zip(context['index'], context['X']))[:5]:\n",
+    "    print(\"entry at {} has value {}\".format(i, x))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### rolling_window_sequences\n",
+    "this primitive generates many sub-sequences of the original sequence. it uses a rolling window approach to create the sub-sequences out of time series data.\n",
+    "\n",
+    "* **input**:\n",
+    "    * `X` n-dimensional sequence to iterate over.\n",
+    "    * `y` 1-dimensional target sequence.\n",
+    "    * `index` array containing the index values of X.\n",
+    "* **output**:\n",
+    "    * `X` input sequences.\n",
+    "    * `y` target sequences.\n",
+    "    * `index` first index value of each input sequence.\n",
+    "    * `target_index` first index value of each target sequence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['index', 'centroids', 'X', 'y', 'target_index'])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 3\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X shape = (697, 50, 1)\n",
+      "y shape = (697, 1)\n",
+      "X index shape = (697,)\n",
+      "y index shape = (697,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# after slicing X into multiple sub-sequences\n",
+    "# we obtain a 3 dimensional matrix X where\n",
+    "# the shape indicates (# slices, window size, 1)\n",
+    "# and similarly y is (# slices, target size)\n",
+    "\n",
+    "print(\"X shape = {}\\ny shape = {}\\nX index shape = {}\\ny index shape = {}\".format(\n",
+    "    context['X'].shape, context['y'].shape, context['index'].shape, context['target_index'].shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### format as string\n",
+    "this primitive converts each sequence of scalar values into string.\n",
+    "\n",
+    "* **input**: `X` which is an n-dimensional sequence of values.\n",
+    "* **output**: `X` which is a string representation version of X."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['index', 'centroids', 'target_index', 'X', 'y'])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 4\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'78,65,64,43,40,35,30,32,15,19,16,12,10,6,3,7,14,13,38,71,55,66,83,73,62,64,56,47,38,46,37,17,24,24,23,13,9,8,6,6,12,15,37,73,57,69,60,63,56,43'"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(context['X']).flatten().tolist()[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### HF\n",
+    "this primitive prompts a huggingface model to forecast the next steps.\n",
+    "\n",
+    "* **input**: `X` input sequence.\n",
+    "* **output**: `y_hat` predicted sequence.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "<b>Warning:</b> this step is time consuming depending on the number of windows.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 697/697 [08:19<00:00,  1.39it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['index', 'centroids', 'target_index', 'X', 'y', 'y_hat'])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 5\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[',35,60,64,54,51', ',38,48,31,35,31'],\n",
+       " [',82,20,42,32,46', ',86,118,82,96,4'],\n",
+       " [',62,13,59,76,66', ',60,59,44,56,59'],\n",
+       " [',34,25,11,9,42,', ',76,52,82,70,46'],\n",
+       " [',30,5,6,12,8,12', ',72,46,20,19,27']]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['y_hat'][:5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### format as integer\n",
+    "this primitive converts each sequence of string values into integers.\n",
+    "\n",
+    "* **input**: `y_hat` which is a sequence of string values.\n",
+    "* **output**: `y_hat` which is an integer representation version of y_hat."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['index', 'centroids', 'target_index', 'y_hat', 'X', 'y'])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 6\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[35.],\n",
+       "        [38.]],\n",
+       "\n",
+       "       [[82.],\n",
+       "        [86.]],\n",
+       "\n",
+       "       [[62.],\n",
+       "        [60.]],\n",
+       "\n",
+       "       [[34.],\n",
+       "        [76.]],\n",
+       "\n",
+       "       [[30.],\n",
+       "        [72.]]])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['y_hat'][:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(697, 2, 1)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# (num_windows, num_samples, pred_length)\n",
+    "context['y_hat'].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Cluster2Scalar\n",
+    "this primitive converts predicted cluster indices back to float values using the centroids from Scalar2Cluster.\n",
+    "\n",
+    "* **input**:\n",
+    "    * `y_hat` sequence of integer cluster indices.\n",
+    "    * `centroids` sorted centroid arrays from Scalar2Cluster.\n",
+    "* **output**: `y_hat` which is a transformed version of `y_hat` in float."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['index', 'centroids', 'target_index', 'y_hat', 'X', 'y'])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 7\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[0.29281093],\n",
+       "        [0.31340244]],\n",
+       "\n",
+       "       [[0.63463164],\n",
+       "        [0.66663686]],\n",
+       "\n",
+       "       [[0.47479945],\n",
+       "        [0.46351018]],\n",
+       "\n",
+       "       [[0.28631282],\n",
+       "        [0.57098036]],\n",
+       "\n",
+       "       [[0.25879724],\n",
+       "        [0.54823709]]])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['y_hat'][:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(697, 2, 1)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['y_hat'].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### aggregate_rolling_window\n",
+    "this primitive aggregates multiple horizon predictions into a single representation.\n",
+    "\n",
+    "* **input**:\n",
+    "    * `y_hat` n-dimensional sequence of forecasted values.\n",
+    "    * `agg` aggregation method, \"median\" by default.\n",
+    "* **output**: `y_hat` one-dimensional output sequence depicting the aggregated value of forecasts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['index', 'centroids', 'target_index', 'y_hat', 'X', 'y'])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 8\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(697,)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['y_hat'].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### reshape\n",
+    "reshape `y_hat` sequences\n",
+    "\n",
+    "* **input**: `y_hat` forecasted and aggregated sequences.\n",
+    "* **output**: `y_hat` reshaped sequences."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['index', 'centroids', 'target_index', 'y_hat', 'X', 'y'])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 9\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(697, 1)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['y_hat'].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### regression_errors\n",
+    "this primitive computes the point-wise difference between `y` and `y_hat`.\n",
+    "\n",
+    "* **input**:\n",
+    "    * `y` target sequences.\n",
+    "    * `y_hat` forecasted sequences.\n",
+    "* **output**: `errors` computed errors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['index', 'centroids', 'target_index', 'y_hat', 'X', 'y', 'errors'])"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 10\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(697, 1)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['y_hat'].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### find anomalies\n",
+    "this primitive extracts anomalies from sequences of errors following the approach explained in the [related paper](https://arxiv.org/pdf/1802.04431.pdf).\n",
+    "\n",
+    "* **input**\n",
+    "    * `errors` array of errors.\n",
+    "    * `target_index` array of indices of errors.\n",
+    "* **output**: `y` array containing start-index, end-index, score for each anomalous sequence that was found."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['index', 'centroids', 'target_index', 'y_hat', 'errors', 'X', 'y', 'anomalies'])"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 11\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "if context['anomalies'].any():\n",
+    "    pd.DataFrame(context['anomalies'], columns=['start', 'end', 'score'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "index, y, yhat, errors, anomalies = list(map(context.get, ['target_index', 'y', 'y_hat', 'errors', 'anomalies']))\n",
+    "\n",
+    "plt.plot(index, errors, label='error')\n",
+    "plt.legend()\n",
+    "\n",
+    "if anomalies.any():\n",
+    "    plt.axvspan(*anomalies[0][:2], color='r', alpha=0.2, label='detected anomalies')\n",
+    "\n",
+    "plt.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Motivation\n",
+    "\n",
+    "We add `k_means_binning` in order to capture more information from floats. There may be information encoded in the trailing decimals that are important in predicting the signal. \n",
+    "\n",
+    "For example, consider a contrived dataset with elements $[10.0001, 10.0002, 20.0001, 20.0002]$. The transition rule is that the trailing decimal ($0.0001$ vs $0.0002$) is random at every timestamp, and the leading digit ($10$ vs $20$) at time $t+1$ is the trailing digit at time $t$. For example, one valid sequence is:\n",
+    "\n",
+    "$$ 10.0002, 20.0002, 20.0001, 10.0002, 20.0001, 10.0001, 10.0001, \\dots $$\n",
+    "\n",
+    "If we truncate decimals, the sequence looks random between 10 and 20. In contrast, K-means binning can separate the values into clusters that retain the trailing-decimal signal. We will demonstrate that K-means binning is able to predict this sequence with significantly lower mean absolute error."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Make the synthetic dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   timestamp    value\n",
+      "0          0  10.0001\n",
+      "1        100  10.0002\n",
+      "2        200  20.0001\n",
+      "3        300  10.0002\n",
+      "4        400  20.0001\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "WINDOW_SIZE = 100\n",
+    "NUM_WINDOWS = 30\n",
+    "dataset_size = WINDOW_SIZE + NUM_WINDOWS\n",
+    "\n",
+    "pos_elements = [10.0001, 10.0002, 20.0001, 20.0002]\n",
+    "series = [10.0001]\n",
+    "prev_trailing_digit = 1\n",
+    "\n",
+    "for i in range(dataset_size):\n",
+    "    next_trailing_digit = np.random.randint(1,3)\n",
+    "    next_element = pos_elements[(prev_trailing_digit - 1) * 2 + (next_trailing_digit - 1)]\n",
+    "    series.append(next_element)\n",
+    "    prev_trailing_digit = next_trailing_digit\n",
+    "\n",
+    "timestamps = np.arange(len(series)) * 100\n",
+    "\n",
+    "df = pd.DataFrame({\n",
+    "    'timestamp': timestamps,\n",
+    "    'value': series\n",
+    "})\n",
+    "\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run two pipelines on the data: one with K-means clustering as the preprocessing and one with scaling/truncating"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading checkpoint shards: 100%|██████████| 3/3 [00:02<00:00,  1.13it/s]\n",
+      "100%|██████████| 31/31 [00:12<00:00,  2.47it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean absolute error of K-means pipeline: 0.3226258064516128\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from mlblocks import MLPipeline \n",
+    "\n",
+    "pipeline_kmeans = MLPipeline('mistral_detector_kmeans')\n",
+    "pipeline_kmeans.set_hyperparameters({\n",
+    "    \"mlstars.custom.timeseries_preprocessing.time_segments_aggregate#1\": {\n",
+    "        \"interval\": 100\n",
+    "    },\n",
+    "    \"sigllm.primitives.forecasting.custom.rolling_window_sequences#1\": {\n",
+    "        \"window_size\": WINDOW_SIZE\n",
+    "    },\n",
+    "})\n",
+    "\n",
+    "context = pipeline_kmeans.fit(df, start_=0, output_=10)\n",
+    "print(f\"Mean absolute error of K-means pipeline: {np.abs(context['errors']).mean()}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading checkpoint shards: 100%|██████████| 3/3 [00:04<00:00,  1.53s/it]\n",
+      "100%|██████████| 31/31 [00:33<00:00,  1.09s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean absolute error of scaling pipeline: 4.838735483870968\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from mlblocks import MLPipeline \n",
+    "\n",
+    "pipeline_kmeans = MLPipeline('mistral_detector')\n",
+    "pipeline_kmeans.set_hyperparameters({\n",
+    "    \"mlstars.custom.timeseries_preprocessing.time_segments_aggregate#1\": {\n",
+    "        \"interval\": 100\n",
+    "    },\n",
+    "    \"sigllm.primitives.forecasting.custom.rolling_window_sequences#1\": {\n",
+    "        \"window_size\": WINDOW_SIZE\n",
+    "    },\n",
+    "})\n",
+    "\n",
+    "context = pipeline_kmeans.fit(df, start_=0, output_=10)\n",
+    "print(f\"Mean absolute error of scaling pipeline: {np.abs(context['errors']).mean()}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we can see, the K-means pipeline is much better at making predictions on this dataset than the typical scaling pipeline, at 0.32 MAE vs 4.84 MAE. This may not hold for all datasets, but it shows that K-means has an advantage for some subset of datasets."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "orion310",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}