Merge branch 'main' into docs-structure

Nixtla · May 3, 2024 · 88636c2 · 88636c2
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diff --git a/nbs/docs/2_capabilities/anomaly_detection/01_quickstart.ipynb b/nbs/docs/2_capabilities/anomaly_detection/01_quickstart.ipynb
diff --git a/nbs/docs/2_capabilities/anomaly_detection/02_confidence_levels.ipynb b/nbs/docs/2_capabilities/anomaly_detection/02_confidence_levels.ipynb
diff --git a/nbs/docs/2_capabilities/anomaly_detection/03_anomaly_detection_date_features.ipynb b/nbs/docs/2_capabilities/anomaly_detection/03_anomaly_detection_date_features.ipynb
diff --git a/nbs/docs/2_capabilities/anomaly_detection/04_anomaly_exogenous.ipynb b/nbs/docs/2_capabilities/anomaly_detection/04_anomaly_exogenous.ipynb
diff --git a/nbs/docs/2_capabilities/forecast/01_quickstart.ipynb b/nbs/docs/2_capabilities/forecast/01_quickstart.ipynb
diff --git a/nbs/docs/2_capabilities/forecast/02_holidays_special_dates.ipynb b/nbs/docs/2_capabilities/forecast/02_holidays_special_dates.ipynb
@@ -0,0 +1,139 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Add holidays and special dates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1: Create an instance of `NixtlaClient`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nixtla import NixtlaClient"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nixtla_client = NixtlaClient(\n",
+    "    # defaults to os.environ.get(\"NIXTLA_API_KEY\")\n",
+    "    api_key = 'my_api_key_provided_by_nixtla'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "from dotenv import load_dotenv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "load_dotenv()\n",
+    "nixtla_client = NixtlaClient()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Get country holidays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from nixtla.date_features import CountryHolidays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c_holidays = CountryHolidays(countries=['US'])\n",
+    "periods = 365 * 1\n",
+    "dates = pd.date_range(end='2023-09-01', periods=periods)\n",
+    "holidays_df = c_holidays(dates)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Specify special dates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from nixtla.date_features import SpecialDates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "special_dates = SpecialDates(\n",
+    "    special_dates={\n",
+    "        'Important Dates': ['2021-02-26', '2020-02-26'],\n",
+    "        'Very Important Dates': ['2021-01-26', '2020-01-26', '2019-01-26']\n",
+    "    }\n",
+    ")\n",
+    "periods = 365 * 1\n",
+    "dates = pd.date_range(end='2023-09-01', periods=periods)\n",
+    "special_dates_df = special_dates(dates)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/nbs/docs/2_capabilities/forecast/03_exogenous_variables.ipynb b/nbs/docs/2_capabilities/forecast/03_exogenous_variables.ipynb
@@ -0,0 +1,145 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Add exogenous variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1: Create an instance of `NixtlaClient`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nixtla import NixtlaClient"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nixtla_client = NixtlaClient(\n",
+    "    # defaults to os.environ.get(\"NIXTLA_API_KEY\")\n",
+    "    api_key = 'my_api_key_provided_by_nixtla'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "from dotenv import load_dotenv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "load_dotenv()\n",
+    "nixtla_client = NixtlaClient()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Load data with exogenous variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/electricity-short-with-ex-vars.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Load dataset with future values of exogenous variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "future_ex_vars_df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/electricity-short-future-ex-vars.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4: Forecast"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:nixtla.nixtla_client:Validating inputs...\n",
+      "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
+      "INFO:nixtla.nixtla_client:Inferred freq: H\n",
+      "INFO:nixtla.nixtla_client:Using the following exogenous variables: Exogenous1, Exogenous2, day_0, day_1, day_2, day_3, day_4, day_5, day_6\n",
+      "INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n"
+     ]
+    }
+   ],
+   "source": [
+    "forecast_df = nixtla_client.forecast(\n",
+    "    df=df, \n",
+    "    X_df=future_ex_vars_df, \n",
+    "    h=24,\n",
+    "    id_col='unique_id',\n",
+    "    target_col='y',\n",
+    "    time_col='ds'\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}