diff --git a/requirements.txt b/requirements.txt
index 91804d4..0a85469 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -6,13 +6,20 @@ scipy
 fastapi
 requests
 
-pyFTS @ git+https://git.athene.tech/sam/pyFTS.git
-# pip install -U --force-reinstall --no-deps -e git+https://git.athene.tech/sam/pyFTS.git#egg=pyFTS
+xgboost-cpu
+# pip3 install -U --force-reinstall torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
 
 scikit-learn
 keras
 tensorflow
 
+darts
+
+pyFTS @ git+https://git.athene.tech/sam/pyFTS.git
+# pip install -U --force-reinstall --no-deps -e git+https://git.athene.tech/sam/pyFTS.git#egg=pyFTS
+
+
+
 influxdb-client
 
-darts
\ No newline at end of file
+
diff --git a/tutorial/darts/anomaly.ipynb b/tutorial/darts/anomaly.ipynb
new file mode 100644
index 0000000..b21a2bc
--- /dev/null
+++ b/tutorial/darts/anomaly.ipynb
@@ -0,0 +1,84 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Anomaly Detection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Darts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Загрузите многомерный ряд, обрежьте его, сохраните 2 компонента, разделите поезд и наборы проверки:\n",
+    "\n",
+    "from darts.datasets import ETTh2Dataset\n",
+    "\n",
+    "series = ETTh2Dataset().load()[:10000][[\"MUFL\", \"LULL\"]]\n",
+    "train, val = series.split_before(0.6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Создайте систему оценки аномалий k-средних, \n",
+    "# обучите ее на наборе поездов и используйте в проверочном наборе для получения оценок аномалий:\n",
+    "\n",
+    "from darts.ad import KMeansScorer\n",
+    "\n",
+    "scorer = KMeansScorer(k=2, window=5)\n",
+    "scorer.fit(train)\n",
+    "anom_score = scorer.score(val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Создайте детектор бинарных аномалий и обучите его на основе оценок поезда, а затем используйте его на результатах проверки, чтобы получить классификацию бинарных аномалий:\n",
+    "\n",
+    "from darts.ad import QuantileDetector\n",
+    "\n",
+    "detector = QuantileDetector(high_quantile=0.99)\n",
+    "detector.fit(scorer.score(train))\n",
+    "binary_anom = detector.detect(anom_score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}