{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## ARMA Modeling: Forecasting\n", "\n", "**Functions**\n", "\n", "`tsa.SARIMAX.forecast`\n", "\n", "### Exercise 70\n", "Produce 1-step forecasts from your preferred model in the previous exercise, as well\n", "as a random-walk model. \n", "\n", "1. Are the forecasts objectively accurate?\n", "2. Compare these forecasts to the random walk models using MSE and MAE.\n", "\n", "**Note**: Use 50% of the sample to estimate the model and 50% to evaluate it." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2023-09-28T12:33:12.877424Z", "iopub.status.busy": "2023-09-28T12:33:12.877424Z", "iopub.status.idle": "2023-09-28T12:33:13.735592Z", "shell.execute_reply": "2023-09-28T12:33:13.735592Z" } }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 71\n", "\n", "Produce 3-step forecasts from the models selected in the previous exercises as well as a random walk model.\n", "\n", "1. Compare these forecasts to the random walk models using MSE and MAE.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2023-09-28T12:33:17.665296Z", "iopub.status.busy": "2023-09-28T12:33:17.665296Z", "iopub.status.idle": "2023-09-28T12:33:17.864965Z", "shell.execute_reply": "2023-09-28T12:33:17.864965Z" } }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.5" }, "pycharm": { "stem_cell": { "cell_type": "raw", "metadata": { "collapsed": false }, "source": [] } } }, "nbformat": 4, "nbformat_minor": 4 }