080_validation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6de758ee-a0d2-4b3f-acff-eed419dd17c5",
   "metadata": {},
   "source": [
    "# Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d267032-535b-4b7b-b7d3-d2db8f673af6",
   "metadata": {},
   "source": [
    "One of the primary challenges in time series forecasting is the inherent uncertainty and variability over time, making it crucial to validate the accuracy and reliability of the models employed. `TimeGPT` offers the possibility for cross-validation and historical forecasts to help you validate your predictions.\n",
    "\n",
    "### What You Will Learn\n",
    "\n",
    "1. **[Cross-Validation](https://docs.nixtla.io/docs/tutorials-cross_validation)**\n",
    "\n",
    "    - Learn how to perform time series cross-validation across different continuous windows of your data. \n",
    "\n",
    "2. **[Historical Forecasts](https://docs.nixtla.io/docs/tutorials-historical_forecast)**\n",
    "\n",
    "    - Generate in-sample forecasts to validate how `TimeGPT` would have performed in the past, providing insights into the model's accuracy. \n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}