{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Autoregressive Models"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"from mxnet import autograd, nd, gluon, init\n",
"import d2l\n",
"# display routines\n",
"%matplotlib inline\n",
"from matplotlib import pyplot as plt\n",
"from IPython import display\n",
"display.set_matplotlib_formats('svg')\n",
"\n",
"embedding = 4 # embedding dimension for autoregressive model\n",
"T = 1000 # generate a total of 1000 points \n",
"time = nd.arange(0,T)\n",
"x = nd.sin(0.01 * time) + 0.2 * nd.random.normal(shape=(T))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n",
"\n"
],
"text/plain": [
"