{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7eb37c52-8262-4359-a6c1-3214e675fa5d",
   "metadata": {},
   "source": [
    "# Lagplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb5dd3db-78c4-4a01-ba07-37f718f7425a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import hvplot.pandas  # noqa\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7af165e-4231-49ea-81d7-d07ad81a05da",
   "metadata": {},
   "source": [
    "Lag plots are most commonly used to look for patterns in time series data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "815a51ab-5f61-4bea-bb56-e11a0f117718",
   "metadata": {},
   "source": [
    "Given the following time series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "283adbd9-3c42-4cb7-8c57-1d0e9fe1d25f",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(5)\n",
    "x = np.cumsum(np.random.normal(loc=1, scale=5, size=50))\n",
    "s = pd.Series(x, name='Time series')\n",
    "\n",
    "s.hvplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a877a7d-11cb-4bcb-81e4-3936f061ba65",
   "metadata": {},
   "source": [
    "A lag plot with `lag=1` returns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52be318e-fccd-461d-9813-c231b6f2997c",
   "metadata": {},
   "outputs": [],
   "source": [
    "hvplot.plotting.lag_plot(s, lag=1)"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python",
   "pygments_lexer": "ipython3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}